app.py

# Phlay to sari important libararies import kr lo 
import streamlit as st 
import numpy as np 
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# app ki heading
st.write("""
# Explore different ML models and datasets
Daikhtay han kon sa best ha in may say?
""")

# data set k name ak box may daal k sidebar pay laga do
dataset_name = st.sidebar.selectbox(
    'Select Dataset',
    ('Iris', 'Breast Cancer', 'Wine')
)

# or isi k nichay classifier k nam ak dabay may dal do
classifier_name = st.sidebar.selectbox(
    'Select classifier',
    ('KNN', 'SVM', 'Random Forest')
)

#ab hum nay ak function define krna hai dataset ko load krnay k liye
def get_dataset(dataset_name):
    data = None
    if dataset_name == "Iris":
        data = datasets.load_iris()
    elif dataset_name == "Wine":
        data = datasets.load_wine()
    else:
        data = datasets.load_breast_cancer()
    x = data.data
    y = data.target
    return x, y

# ab is function ko bula lay gayn or X, y variable k equal rakh layn gay
X, y = get_dataset(dataset_name)

# ab hum apnay data set ki shape ko ap pay print kr dayn gay 
st.write('Shape of dataset:', X.shape)
st.write('number of classes:', len(np.unique(y)))


# next hum different classifier k  parameter ko user input may add karayn gay 
def add_parameter_ui(classifier_name):
    params = dict()  # Create an empty dictionary
    if classifier_name == 'SVM':
        C = st.sidebar.slider('C', 0.01, 10.0)
        params['C'] = C   # its the degree of correct classification
    elif classifier_name == 'KNN':
        K = st.sidebar.slider('K', 1, 15)
        params['K'] = K    # its the number of nearest neighbour
    else:
        max_depth = st.sidebar.slider('max_depth', 2, 15)
        params['max_depth'] = max_depth   # depth of every tree that grow in random forest
        n_estimators = st.sidebar.slider('n_estimators', 1, 100)
        params['n_estimators'] = n_estimators  # number of trees
    return params

# ab is function ko bula lay gayn or params variable k equal rakh layn gay
params = add_parameter_ui(classifier_name)

# ab hum classifier bnayen gay base on classifier_name and params
def get_classifier(classifier_name, params):
    clf = None
    if classifier_name == 'SVM':
        clf = SVC(C=params['C'])
    elif classifier_name == 'KNN':
        clf = KNeighborsClassifier(n_neighbors=params['K'])
    else:
        clf = clf = RandomForestClassifier(n_estimators=params['n_estimators'], 
            max_depth=params['max_depth'], random_state=1234)
    return clf

# ab is function ko bula lay gayn or clf variable k equal rakh layn gay
clf = get_classifier(classifier_name, params)

# ab hum apnay dataset ko test and train data may split kr laytay han by 80/20 ratio
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)

# ab hum nay apnay classifier ki training krni ha 
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

# model ka accuracy score check kr layna ha or isay app pay print kr dayna ha
acc = accuracy_score(y_test, y_pred)
st.write(f'Classifier = {classifier_name}')
st.write(f'Accuracy =', acc)

#### PLOT DATASET ####
# ab hum apnay saray saray features ko 2 dimenssional plot pay draw kr dayn gay using pca 
pca = PCA(2)
X_projected = pca.fit_transform(X)

# ab hum apna data 0 or 1 dimenssion may slice kr kr dayn gay
x1 = X_projected[:, 0]
x2 = X_projected[:, 1]

fig = plt.figure()
plt.scatter(x1, x2,
        c=y, alpha=0.8, 
        cmap='viridis')

plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.colorbar()

#plt.show()
st.pyplot(fig)