Update train.py

train.py

@@ -0,0 +1,81 @@
+'''
+author         : Rupesh Garsondiya 
+github       : @Rupeshgarsondiya
+Organization : L.J University
+'''
+
+
+
+
+
+import pandas as pd
+import streamlit as st
+import numpy as np
+from features.build_features import  *
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+
+
+
+class Model_Train:
+    def __init__(self) -> None:
+        pass
+
+    '''load_data()  fuction use for to get the clean data or feature transformed data '''
+    def load_data(self):
+        pass
+   
+
+    def train_model(self):
+        st.markdown(
+            """
+            <style>
+            body {
+                background-color: lightblue;
+            }
+            </style>
+            """,
+            unsafe_allow_html=True
+        )
+        fe = FeatureEngineering()
+        x_train,x_test,y_train,y_test,pipeline = fe.get_clean_data()
+       
+        
+        # Define the options for the dropdown menu
+        options = ['Logistic Regreesion', 'Random Forest Classifier', 'Decision Tree', 'SVM','KNeighborsClassifier']
+        # Create the dropdown menu
+        with st.container():
+         st.markdown('<div class="dropdown-left">', unsafe_allow_html=True)
+         selected_option = st.sidebar.selectbox('Select Algoritham :', options)
+         st.markdown('</div>', unsafe_allow_html=True)
+
+        S_algo  = object
+        if selected_option== 'Logistic Regreesion':
+            S_algo = LogisticRegression()
+            S_algo.fit(x_train,y_train)
+            ypred = S_algo.predict(x_test)
+        elif selected_option=='Random Forest Classifier':
+            S_algo = RandomForestClassifier(n_estimators=200,n_jobs=-1,verbose=True,max_depth=2)
+            S_algo.fit(x_train,y_train)
+            ypred1 = S_algo.predict(x_test)
+        elif selected_option=='Decision Tree':
+            S_algo = DecisionTreeClassifier(max_depth=4,max_leaf_nodes=5,min_samples_split=50)
+            S_algo.fit(x_train,y_train)
+            ypred2 = S_algo.predict(x_test)
+        elif selected_option =='SVM':
+            S_algo = SVC()
+            S_algo.fit(x_train,y_train)
+            ypred3 = S_algo.predict(x_test)
+        elif selected_option=='KNeighborsClassifier':
+            S_algo = KNeighborsClassifier()
+            S_algo.fit(x_train,y_train)
+            ypred4  = S_algo.predict(x_test)           
+        else:
+            pass
+
+        return S_algo,pipeline