Delete src/train.py

src/train.py

@@ -1,81 +0,0 @@
-'''
-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