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Modified regressor model
Browse files- __pycache__/regression.cpython-310.pyc +0 -0
- app.py +47 -18
- regression.py +124 -2
__pycache__/regression.cpython-310.pyc
CHANGED
Binary files a/__pycache__/regression.cpython-310.pyc and b/__pycache__/regression.cpython-310.pyc differ
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app.py
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@@ -1,16 +1,12 @@
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from classification import ClassificationModels
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from regression import RegressionModels
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from resume import Resume
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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from sklearn.compose import ColumnTransformer
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from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
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'''
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import pandas as pd
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import warnings
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import streamlit as st
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@@ -312,16 +308,6 @@ def classification():
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#spectra_df1 = spectra_df1.drop(columns=['Disease'])
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st.write(spectra_df1.head(5))
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st.divider()
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model_dict ={
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"Naive Bayes Classifier":'GaussianNB()',
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"Logistic Regression":'LogisticRegression()',
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"Decision Tree":'DecisionTreeClassifier()',
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"Random Forests":'RandomForestClassifier()',
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"SVM":'SVC()',
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"KNN":'KNeighborsClassifier()',
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"K- Means Clustering":'KMeans()'
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}
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X= spectra_df1
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if max_key == "Naive Bayes Classifier":
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models.split_data()
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# Train and evaluate selected models
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for model_name in selected_models:
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st.subheader(f"Model: {model_name}")
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models.fit(model_name)
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y_pred = models.train(model_name)
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mse, r2 = models.evaluate(model_name)
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st.write(f"MSE: {mse}")
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st.write(f"R-squared: {r2}")
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def NLP():
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Gemini_Chat,Gemini_Vision,Gemini_PDF, Bert, = st.tabs(['Gemini-Chat','Gemini-Vision',"Gemini-PDF Chat",'ChatBot'])
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from classification import ClassificationModels
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from regression import RegressionModels
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from resume import Resume
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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from sklearn.compose import ColumnTransformer
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from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
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import pandas as pd
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import warnings
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import streamlit as st
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#spectra_df1 = spectra_df1.drop(columns=['Disease'])
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st.write(spectra_df1.head(5))
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st.divider()
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X= spectra_df1
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if max_key == "Naive Bayes Classifier":
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models.split_data()
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# Train and evaluate selected models
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best_model = None
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best_metric = float('inf') # Initialize with a high value for MSE (lower is better)
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for model_name in selected_models:
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# st.subheader(f"Model: {model_name}")
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models.fit(model_name)
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y_pred = models.train(model_name)
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mse, r2 = models.evaluate(model_name)
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# st.write(f"MSE: {mse}")
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# st.write(f"R-squared: {r2}")
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# Update best model based on MSE
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if r2 < best_metric:
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best_model = model_name
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best_metric = r2
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# Perform testing based on the best model
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if best_model:
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st.subheader(f"Best Model: {best_model}")
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test_mse, test_r2 = models.evaluate(best_model)
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st.write(f"Test MSE: {test_mse}")
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st.write(f"Test R-squared: {test_r2}")
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# You can also visualize the predictions vs. true values, residual plots, etc. here
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else:
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st.write("No best model selected.")
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with test:
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st.title("Regression / Test")
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spectra_1 = st.file_uploader("Upload file test the model", type={"csv", "txt"})
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if spectra_1 is not None:
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spectra_df1 = pd.read_csv(spectra_1)
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st.write(spectra_df1.head(5))
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st.divider()
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st.write("models",models)
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# models = RegressionModels()
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if best_model:
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# st.subheader(f"Best Model: {best_model}")
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st.write("best model", best_model)
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y_pred= models.predict(model_name = best_model,X = spectra_df1)
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# st.write(f"Test MSE: {test_mse}")
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st.write(f"Y pred is : {max(y_pred)}")
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# You can also visualize the predictions vs. true values, residual plots, etc. here
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else:
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st.write("No best model selected.")
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def NLP():
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Gemini_Chat,Gemini_Vision,Gemini_PDF, Bert, = st.tabs(['Gemini-Chat','Gemini-Vision',"Gemini-PDF Chat",'ChatBot'])
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regression.py
CHANGED
@@ -10,6 +10,125 @@ from sklearn.svm import SVR
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from xgboost import XGBRegressor
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from lightgbm import LGBMRegressor
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from sklearn.metrics import mean_squared_error, r2_score
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class RegressionModels:
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def __init__(self):
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model_pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', model)
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return model_pipeline.predict(X)
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from xgboost import XGBRegressor
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from lightgbm import LGBMRegressor
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from sklearn.metrics import mean_squared_error, r2_score
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import streamlit as st
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class RegressionModels:
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def __init__(self):
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self.data = None
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self.X_train = None
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self.X_test = None
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self.y_train = None
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self.y_test = None
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self.column_transformer = None # Initialize as None
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self.models = {
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'Linear Regression': LinearRegression(),
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'Polynomial Regression': LinearRegression(),
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'Ridge Regression': Ridge(),
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'Lasso Regression': Lasso(),
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'ElasticNet Regression': ElasticNet(),
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'Logistic Regression': LogisticRegression(),
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'Decision Tree Regression': DecisionTreeRegressor(),
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'Random Forest Regression': RandomForestRegressor(),
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'Gradient Boosting Regression': GradientBoostingRegressor(),
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'Support Vector Regression (SVR)': SVR(),
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'XGBoost': XGBRegressor(),
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'LightGBM': LGBMRegressor()
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}
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def add_data(self, X, y):
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self.data = (X, y)
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def split_data(self, test_size=0.2, random_state=None):
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if self.data is None:
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raise ValueError("No data provided. Use add_data method to add data first.")
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X, y = self.data
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self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
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def build_preprocessor(self):
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if self.column_transformer is not None:
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return self.column_transformer # Return the existing fitted ColumnTransformer
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else:
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# Separate numerical and categorical columns
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numeric_features = self.X_train.select_dtypes(include=['int64', 'float64']).columns
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categorical_features = self.X_train.select_dtypes(include=['object']).columns
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# Define transformers for numerical and categorical data
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numeric_transformer = Pipeline(steps=[
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('imputer', SimpleImputer(strategy='mean')),
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('scaler', StandardScaler())
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])
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categorical_transformer = Pipeline(steps=[
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('imputer', SimpleImputer(strategy='most_frequent')),
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('onehot', OneHotEncoder(handle_unknown='ignore'))
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])
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# Combine transformers using ColumnTransformer
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self.column_transformer = ColumnTransformer(
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transformers=[
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('num', numeric_transformer, numeric_features),
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('cat', categorical_transformer, categorical_features)
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])
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return self.column_transformer
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def fit(self, model_name):
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if self.X_train is None or self.y_train is None:
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raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
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model = self.models[model_name]
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preprocessor = self.build_preprocessor()
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model_pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', model)
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])
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model_pipeline.fit(self.X_train, self.y_train)
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def train(self, model_name):
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if self.X_train is None or self.y_train is None or self.X_test is None:
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raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
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model = self.models[model_name]
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preprocessor = self.build_preprocessor()
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model_pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', model)
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])
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model_pipeline.fit(self.X_train, self.y_train)
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y_pred = model_pipeline.predict(self.X_test)
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return y_pred
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def evaluate(self, model_name):
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if self.X_test is None or self.y_test is None:
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raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
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model = self.models[model_name]
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preprocessor = self.build_preprocessor()
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model_pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', model)
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])
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model_pipeline.fit(self.X_train, self.y_train)
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y_pred = model_pipeline.predict(self.X_test)
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mse = mean_squared_error(self.y_test, y_pred)
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r2 = r2_score(self.y_test, y_pred)
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return mse, r2
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def predict(self, model_name, X):
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model = self.models[model_name]
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preprocessor = self.build_preprocessor() # Ensure that the ColumnTransformer is fitted
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model_pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', model)
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])
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return model_pipeline.predict(X)
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'''
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class RegressionModels:
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def __init__(self):
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model_pipeline = Pipeline(steps=[
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('preprocessor', preprocessor),
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('model', model)
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])
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st.write("Model", model)
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st.write(X.head(4))
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return model_pipeline.predict(X)
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'''
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