| import dash
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| from dash import html, dcc, Input, Output
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| import dash_bootstrap_components as dbc
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| import plotly.express as px
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| import pandas as pd
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| from sklearn.datasets import load_breast_cancer
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|
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| dash.register_page(__name__, path="/data-analysis", name="Data Analysis")
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| data = load_breast_cancer()
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| df = pd.DataFrame(data.data, columns=data.feature_names)
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| df["target"] = data.target
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| df["target_label"] = df["target"].map({0: "Malignant", 1: "Benign"})
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| important_features = [
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| "texture error", "area error", "smoothness error",
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| "concavity error", "symmetry error",
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| "fractal dimension error", "worst concavity"
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| ]
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| top4_features = important_features[:4]
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| layout = html.Div(
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| style={"padding": "30px"},
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| children=[
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| html.H2("Data Analysis", className="page-title"),
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| dbc.Row(
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| [
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| dbc.Col(
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| dcc.Dropdown(
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| id="diagnosis-filter",
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| options=[
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| {"label": "All", "value": "all"},
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| {"label": "Benign", "value": 1},
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| {"label": "Malignant", "value": 0},
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| ],
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| value="all",
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| clearable=False,
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| placeholder="Filter by Diagnosis",
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| style={
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| "background": "linear-gradient(135deg, #ff77b4, #e61227)",
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| "color": "#fff",
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| "border-radius": "8px",
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| "padding": "5px"
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| }
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| ),
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| width=3
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| ),
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| dbc.Col(
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| dcc.Dropdown(
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| id="feature-select",
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| options=[{"label": f, "value": f} for f in important_features],
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| value=important_features[0],
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| clearable=False,
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| placeholder="Select Feature",
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| style={
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| "background": "linear-gradient(135deg, #ff77b4, #e61227)",
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| "color": "#fff",
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| "border-radius": "8px",
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| "padding": "5px"
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| }
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| ),
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| width=3
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| )
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| ],
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| className="mb-4",
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| align="center",
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| justify="start",
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| style={"gap": "20px"}
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| ),
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| dbc.Card(
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| dbc.CardBody(dcc.Graph(id="heatmap-plot")),
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| className="custom-card shadow mb-4"
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| ),
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| dbc.Row(
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| [
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| dbc.Col(dcc.Graph(id=f"pairplot-{i}"), md=6)
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| for i in range(6)
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| ],
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| className="mb-4",
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| ),
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| dbc.Card(
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| dbc.CardBody(dcc.Graph(id="box-plot")),
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| className="custom-card shadow mb-4"
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| ),
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| dbc.Card(
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| dbc.CardBody(dcc.Graph(id="violin-plot")),
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| className="custom-card shadow mb-4"
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| ),
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| dbc.Card(
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| dbc.CardBody(dcc.Graph(id="histogram-plot")),
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| className="custom-card shadow mb-4"
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| ),
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| ]
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| )
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| from dash import callback
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| @callback(
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| Output("heatmap-plot", "figure"),
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| [Output(f"pairplot-{i}", "figure") for i in range(6)],
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| Output("box-plot", "figure"),
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| Output("violin-plot", "figure"),
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| Output("histogram-plot", "figure"),
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| Input("diagnosis-filter", "value"),
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| Input("feature-select", "value")
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| )
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| def update_plots(filter_value, selected_feature):
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| filtered = df.copy()
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| if filter_value != "all":
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| filtered = filtered[filtered["target"] == filter_value]
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| corr = filtered[important_features].corr()
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| fig_heatmap = px.imshow(corr, color_continuous_scale="RdPu")
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| from itertools import combinations
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| pair_figs = []
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| for f1, f2 in combinations(top4_features, 2):
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| fig = px.scatter(
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| filtered,
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| x=f1,
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| y=f2,
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| color="target_label",
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| color_discrete_map={"Benign": "#ff77b4", "Malignant": "#9c005d"},
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| )
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| fig.update_layout(
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| xaxis_tickangle=45,
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| yaxis_tickangle=0,
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| margin=dict(l=40, r=20, t=20, b=40),
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| )
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| pair_figs.append(fig)
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| fig_box = px.box(
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| filtered,
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| y=selected_feature,
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| color="target_label",
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| color_discrete_map={"Benign": "#ff77b4", "Malignant": "#9c005d"},
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| points="all"
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| )
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| fig_violin = px.violin(
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| filtered,
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| y=selected_feature,
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| color="target_label",
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| color_discrete_map={"Benign": "#ff77b4", "Malignant": "#9c005d"},
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| box=True,
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| points="all"
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| )
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| fig_hist = px.histogram(
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| filtered,
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| x=selected_feature,
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| color="target_label",
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| barmode="overlay",
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| opacity=0.7,
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| color_discrete_map={"Benign": "#ff77b4", "Malignant": "#9c005d"}
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| )
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| return fig_heatmap, *pair_figs, fig_box, fig_violin, fig_hist
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|
