vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
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Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
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基于 SOC 职业分类
| name | sweetviz-2-target-variable-analysis |
| description | Sub-skill of sweetviz: 2. Target Variable Analysis. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Binary Target Analysis:
import sweetviz as sv
import pandas as pd
import numpy as np
# Create dataset with target variable
np.random.seed(42)
n = 3000
df = pd.DataFrame({
"feature_1": np.random.randn(n),
"feature_2": np.random.exponential(10, n),
"feature_3": np.random.choice(["A", "B", "C"], n),
"feature_4": np.random.randint(1, 100, n),
"target": np.random.choice([0, 1], n, p=[0.7, 0.3])
})
# Analyze with target variable
# Shows how each feature relates to the target
report = sv.analyze(
source=df,
target_feat="target" # Specify target column
)
report.show_html("target_analysis.html")
Continuous Target Analysis:
import sweetviz as sv
import pandas as pd
import numpy as np
# Regression target example
np.random.seed(42)
n = 2000
# Features that affect the target
x1 = np.random.randn(n)
x2 = np.random.exponential(5, n)
x3 = np.random.choice([0, 1], n)
# Target is a function of features + noise
target = 10 + 2*x1 + 0.5*x2 + 3*x3 + np.random.randn(n)
df = pd.DataFrame({
"feature_linear": x1,
"feature_exp": x2,
"feature_binary": x3,
"feature_noise": np.random.randn(n), # Unrelated feature
"price": target # Continuous target
})
# Analyze with continuous target
report = sv.analyze(
source=df,
target_feat="price"
)
report.show_html("regression_target_analysis.html")
Multi-class Target:
import sweetviz as sv
import pandas as pd
import numpy as np
np.random.seed(42)
n = 2500
df = pd.DataFrame({
"feature_1": np.random.randn(n),
"feature_2": np.random.uniform(0, 100, n),
"category": np.random.choice(["A", "B", "C"], n),
"class_label": np.random.choice(
["Class_A", "Class_B", "Class_C", "Class_D"],
n, p=[0.4, 0.3, 0.2, 0.1]
)
})
# Multi-class target analysis
report = sv.analyze(
source=df,
target_feat="class_label"
)
report.show_html("multiclass_analysis.html")