Datasets:
ArXiv:
License:
File size: 3,631 Bytes
6822471 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
{
"name": "34_Customer_Segmentation_KMeans_CustomerSegmentation_ML",
"query": "I need to create a customer segmentation system using the K-means clustering algorithm with the Kaggle Customer Segmentation dataset. Start by standardizing the data in `src/data_loader.py`, then use the elbow method to determine the optimal number of clusters and save the elbow plot to `results/figures/elbow.jpg`. Implement the K-means algorithm in `src/model.py`. Save the cluster centers in `results/metrics/cluster_centers.txt`. Visualize the segmentation results using seaborn and save the plot as `results/figures/customer_segmentation.png`. Create an interactive Dash dashboard allowing dynamic exploration of the segments.",
"tags": [
"Unsupervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Kaggle Customer Segmentation\" dataset is used, including data loading and preparation in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data is standardized to ensure feature values are within the same range in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
1
],
"criteria": "The elbow method is used to determine the optimal number of clusters. Please save the elbow plot to `results/figures/elbow.jpg`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "The K-means clustering algorithm is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
2,
3
],
"criteria": "Cluster centers are saved in `results/metrics/cluster_centers.txt`.",
"category": "Save Trained Model",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
2,
3,
4
],
"criteria": "The Customer segmentation is visualized using \"seaborn,\" with the plot saved as `results/figures/customer_segmentation.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
2,
3,
4
],
"criteria": "An interactive dashboard which allows dynamic exploration of the segments is created using \"Dash\".",
"category": "Human Computer Interaction",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The elbow plot clearly shows how the optimal number of clusters is determined.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": " The system properly manages the launch and termination of the dashboard.",
"satisfied": null
}
],
"is_kaggle_api_needed": true,
"is_training_needed": true,
"is_web_navigation_needed": false
} |