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New AI app.

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Files changed (10) hide show
  1. .gitattributes +2 -35
  2. .gitignore +1 -0
  3. .python-version +1 -0
  4. LICENSE +13 -0
  5. README.md +6 -6
  6. app.py +163 -0
  7. dataset/colorectal-cancer-dataset.zip +3 -0
  8. dataset/colorectal_cancer_dataset.csv +3 -0
  9. pyproject.toml +15 -0
  10. uv.lock +680 -0
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- *tfevents* filter=lfs diff=lfs merge=lfs -text
 
1
+ dataset/colorectal-cancer-dataset.zip filter=lfs diff=lfs merge=lfs -text
2
+ dataset/colorectal_cancer_dataset.csv filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.gitignore ADDED
@@ -0,0 +1 @@
 
 
1
+ __pycache__
.python-version ADDED
@@ -0,0 +1 @@
 
 
1
+ 3.13
LICENSE ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ BSD 3 Clause License
2
+
3
+ Copyright © 2025 Oliver Ladores
4
+
5
+ Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
6
+
7
+ 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
8
+
9
+ 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
10
+
11
+ 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
12
+
13
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
README.md CHANGED
@@ -1,10 +1,10 @@
1
  ---
2
- title: CCS229 Colorectal-Cancer-Predictor-App
3
- emoji: 🦀
4
- colorFrom: blue
5
- colorTo: red
6
  sdk: docker
 
7
  pinned: false
 
8
  ---
9
-
10
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: CCS229 Colorectal
3
+ emoji: 🍩
4
+ colorFrom: yellow
5
+ colorTo: green
6
  sdk: docker
7
+ port: 7860
8
  pinned: false
9
+ short_description: Colorectal cancer predictor
10
  ---
 
 
app.py ADDED
@@ -0,0 +1,163 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import marimo
2
+
3
+ __generated_with = "0.11.13"
4
+ app = marimo.App(width="medium")
5
+
6
+
7
+ @app.cell
8
+ def _():
9
+ import marimo as mo
10
+ import polars as pl
11
+ return mo, pl
12
+
13
+
14
+ @app.cell
15
+ def _(pl):
16
+ dataset = pl.read_csv('./dataset/colorectal_cancer_dataset.csv')
17
+ dataset
18
+ return (dataset,)
19
+
20
+
21
+ @app.cell
22
+ def _(dataset, pl):
23
+ from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
24
+
25
+ encoder = OneHotEncoder(sparse_output=False)
26
+ ord_encoder = OrdinalEncoder()
27
+ encoded = encoder.fit_transform(dataset.select(['Obesity_BMI', 'Cancer_Stage']))
28
+ ord_encoded = ord_encoder.fit_transform(dataset.select('Survival_5_years'))
29
+ encoded_features = encoder.get_feature_names_out(['Obesity_BMI', 'Cancer_Stage'])
30
+ ord_encoded_features = ord_encoder.get_feature_names_out(['Survival_5_years'])
31
+ encoded_schema = {name: pl.Int8 for name in encoded_features}
32
+ ord_encoded_schema = {name: pl.Int8 for name in ord_encoded_features}
33
+ dataset_encoded_parts = pl.DataFrame(encoded, schema=encoded_schema)
34
+ dataset_ord_encoded_parts = pl.DataFrame(ord_encoded, schema=ord_encoded_schema)
35
+ dataset_encoded = dataset.with_columns(dataset_encoded_parts).with_columns(dataset_ord_encoded_parts)
36
+ dataset_encoded
37
+ return (
38
+ OneHotEncoder,
39
+ OrdinalEncoder,
40
+ dataset_encoded,
41
+ dataset_encoded_parts,
42
+ dataset_ord_encoded_parts,
43
+ encoded,
44
+ encoded_features,
45
+ encoded_schema,
46
+ encoder,
47
+ ord_encoded,
48
+ ord_encoded_features,
49
+ ord_encoded_schema,
50
+ ord_encoder,
51
+ )
52
+
53
+
54
+ @app.cell
55
+ def _(dataset_encoded, encoded_features, mo):
56
+ from sklearn.linear_model import LogisticRegression
57
+ from sklearn.naive_bayes import BernoulliNB
58
+ from sklearn.tree import DecisionTreeClassifier
59
+ from sklearn.model_selection import train_test_split
60
+ from sklearn.metrics import accuracy_score, precision_score, classification_report, confusion_matrix
61
+
62
+ X = dataset_encoded.select(['Age', 'Tumor_Size_mm'] + encoded_features.tolist())
63
+ y = dataset_encoded.select(['Survival_5_years'])
64
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=33)
65
+ logreg = LogisticRegression()
66
+ y_pred_logreg = logreg.fit(X_train, y_train).predict(X_test)
67
+ bnb = BernoulliNB()
68
+ y_pred_bnb = bnb.fit(X_train, y_train).predict(X_test)
69
+ dectree = DecisionTreeClassifier()
70
+ y_pred_dectree = dectree.fit(X_train, y_train).predict(X_test)
71
+
72
+ mo.md(f"""
73
+ # Logistic Regression
74
+
75
+ Accuracy score: {accuracy_score(y_test, y_pred_logreg)}
76
+
77
+ Precision score: {precision_score(y_test, y_pred_logreg)}
78
+
79
+ Confusion matrix:
80
+ ```
81
+ {confusion_matrix(y_test, y_pred_logreg)}
82
+ ```
83
+
84
+ Classification report:
85
+ ```
86
+ {classification_report(y_test, y_pred_logreg)}
87
+ ```
88
+
89
+ # Bernoulli Naive Bayes
90
+
91
+ Accuracy score: {accuracy_score(y_test, y_pred_bnb)}
92
+
93
+ Precision score: {precision_score(y_test, y_pred_bnb)}
94
+
95
+ Confusion matrix:
96
+ ```
97
+ {confusion_matrix(y_test, y_pred_bnb)}
98
+ ```
99
+
100
+ Classification report:
101
+ ```
102
+ {classification_report(y_test, y_pred_bnb)}
103
+ ```
104
+
105
+ # Decision Tree Classifier
106
+
107
+ Accuracy score: {accuracy_score(y_test, y_pred_dectree)}
108
+
109
+ Precision score: {precision_score(y_test, y_pred_dectree)}
110
+
111
+ Confusion matrix:
112
+ ```
113
+ {confusion_matrix(y_test, y_pred_dectree)}
114
+ ```
115
+
116
+ Classification report:
117
+ ```
118
+ {classification_report(y_test, y_pred_dectree)}
119
+ ```
120
+ """)
121
+ return (
122
+ BernoulliNB,
123
+ DecisionTreeClassifier,
124
+ LogisticRegression,
125
+ X,
126
+ X_test,
127
+ X_train,
128
+ accuracy_score,
129
+ bnb,
130
+ classification_report,
131
+ confusion_matrix,
132
+ dectree,
133
+ logreg,
134
+ precision_score,
135
+ train_test_split,
136
+ y,
137
+ y_pred_bnb,
138
+ y_pred_dectree,
139
+ y_pred_logreg,
140
+ y_test,
141
+ y_train,
142
+ )
143
+
144
+
145
+ @app.cell
146
+ def _(dataset_cluster, mo):
147
+ import altair as alt
148
+ chart1 = alt.Chart(dataset_cluster).mark_circle().encode(
149
+ alt.Y('Incidence_Rate_per_100K'),
150
+ alt.X('Mortality_Rate_per_100K'),
151
+ color='Cluster',
152
+ )
153
+ mo.ui.altair_chart(chart1)
154
+ return alt, chart1
155
+
156
+
157
+ @app.cell
158
+ def _():
159
+ return
160
+
161
+
162
+ if __name__ == "__main__":
163
+ app.run()
dataset/colorectal-cancer-dataset.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ size 4118299
dataset/colorectal_cancer_dataset.csv ADDED
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+ size 24997818
pyproject.toml ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ [project]
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+ name = "ccs229-colorectal-cancer-predictor-app"
3
+ version = "0.1.0"
4
+ description = "Add your description here"
5
+ license = "BSD-3-Clause"
6
+ readme = "README.md"
7
+ requires-python = ">=3.13"
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+ dependencies = [
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+ "altair>=5.5.0",
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+ "marimo>=0.11.13",
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+ "pandas>=2.2.3",
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+ "polars>=1.24.0",
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+ "pyarrow>=19.0.1",
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+ "scikit-learn>=1.6.1",
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+ ]
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+ [[package]]
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