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