webapp beginner

Author: Harshil Darji

.gitignore

@@ -1,3 +1,6 @@
+# IntelliJ folders
+.idea/
+
 # IPython Notebook
 .ipynb_checkpoints
 

Web Apps/multiple_datasets_clfs.py

@@ -0,0 +1,75 @@
+# Video link: https://www.youtube.com/watch?v=Klqn--Mu2pE&ab_channel=PythonEngineer
+import numpy as np
+import streamlit as st
+from sklearn import datasets
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, f1_score
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+
+st.title("Beginner ML WebApp")
+st.write(
+    "_You can change the dataset and the classifier from the left sidebar._\n\n---"
+)
+
+dataset = st.sidebar.selectbox(
+    "Choose a dataset:", ("Iris", "Breast cancer", "Wine dataset")
+)
+clf = st.sidebar.selectbox("Choose a classifier:", ("KNN", "SVM", "Random Forest"))
+
+_dataset = {
+    "Iris": datasets.load_iris(),
+    "Breast cancer": datasets.load_breast_cancer(),
+    "Wine dataset": datasets.load_wine(),
+}
+
+
+def get_dataset(dataset_name):
+    _data = _dataset[dataset_name]
+    return _data.data, _data.target
+
+
+def get_clf(clf_name):
+    if clf_name == "KNN":
+        k = st.sidebar.slider("Nearest neighbours to consider:", 1, 15)
+        clf = KNeighborsClassifier(n_neighbors=k)
+    elif clf_name == "SVM":
+        c = st.sidebar.slider("Regularization value:", 0.01, 10.0)
+        clf = SVC(C=c)
+    elif clf_name == "Random Forest":
+        max_depth = st.sidebar.slider("Maximum depth:", 2, 15)
+        n_estimators = st.sidebar.slider("Number of estimators:", 1, 100)
+        clf = RandomForestClassifier(
+            max_depth=max_depth, n_estimators=n_estimators, random_state=42
+        )
+
+    return clf
+
+
+clf = get_clf(clf)
+X, y = get_dataset(dataset)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.25, random_state=42
+)
+clf.fit(X_train, y_train)
+preds = clf.predict(X_test)
+
+acc = accuracy_score(y_test, preds)
+f1_macro = f1_score(y_test, preds, average="macro")
+f1_micro = f1_score(y_test, preds, average="micro")
+
+st.write(
+    f"Dataset:\n"
+    f"#### **{dataset}**\n"
+    f"- Shape: **{X.shape}**\n"
+    f"- Number of classes: **{len(np.unique(y))}**\n---"
+)
+st.write("Classifier (*with params*):\n" "#### **{}**\n---".format(clf))
+st.write(
+    f"Performance:\n\n"
+    f"Accuracy: **{acc}**\n\n"
+    f"F1 Score:\n"
+    f"- Macro: **{f1_macro}**\n"
+    f"- Micro: **{f1_micro}**"
+)