adding new module of regression

app.py

@@ -25,8 +25,45 @@ import time
 #     st.write("Welcome to the Home Page")
 
 def regressor():
-    st.title("About Page")
-    st.write("This is the About Page")
+    train, test = st.tabs(['Train','Test'])
+
+    with train:
+            st.title("Regression/Train data")
+            spectra = st.file_uploader("**Upload file**", type={"csv", "txt"})
+            
+            if spectra is not None:
+                spectra_df = pd.read_csv(spectra)
+                
+                st.write(spectra_df.head(5))
+                # st.write("Headers", spectra_df.columns.tolist())
+                st.write("**Total Rows**", spectra_df.shape[0])
+
+                st.divider()
+
+                option = st.text_input("**Select Output Column**:")
+                st.divider()
+
+                if option:
+                    st.write("**You have selected output column**: ", option)
+
+                    y = spectra_df[option] 
+                    X= spectra_df.drop(option, axis=1)
+
+                                        # Define the columns with your content
+                    col1, col2 = st.columns([4,1], gap="small")
+
+                    # Add content to col1
+                    with col1:
+                        st.write("Train data excluding output")
+                        st.write(X.head(5))
+
+                    # Add content to col2
+                    with col2:
+                        st.write("Output")
+                        st.write(y.head(5))
+
+                    st.divider()
+
 
 def NLP():
     st.title("Contact Page")

regression.py

@@ -0,0 +1,72 @@
+from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, LogisticRegression
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
+from sklearn.svm import SVR
+from xgboost import XGBRegressor
+from lightgbm import LGBMRegressor
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import train_test_split
+from xgboost import XGBRegressor
+
+from lightgbm import LGBMRegressor
+
+
+class RegressionModels:
+    def __init__(self):
+        self.data = None
+        self.X_train = None
+        self.X_test = None
+        self.y_train = None
+        self.y_test = None
+        self.models = {
+            'Linear Regression': LinearRegression(),
+            'Polynomial Regression': LinearRegression(),
+            'Ridge Regression': Ridge(),
+            'Lasso Regression': Lasso(),
+            'ElasticNet Regression': ElasticNet(),
+            'Logistic Regression': LogisticRegression(),
+            'Decision Tree Regression': DecisionTreeRegressor(),
+            'Random Forest Regression': RandomForestRegressor(),
+            'Gradient Boosting Regression': GradientBoostingRegressor(),
+            'Support Vector Regression (SVR)': SVR(),
+            'XGBoost': XGBRegressor(),
+            'LightGBM': LGBMRegressor()
+        }
+        
+    def add_data(self, X, y):
+        self.data = (X, y)
+        
+    def split_data(self, test_size=0.2, random_state=None):
+        if self.data is None:
+            raise ValueError("No data provided. Use add_data method to add data first.")
+        X, y = self.data
+        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)
+        
+    def fit(self, model_name):
+        if self.X_train is None or self.y_train is None:
+            raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
+        model = self.models[model_name]
+        model.fit(self.X_train, self.y_train)
+        
+    def train(self, model_name):
+        if self.X_train is None or self.y_train is None or self.X_test is None:
+            raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
+        model = self.models[model_name]
+        model.fit(self.X_train, self.y_train)
+        y_pred = model.predict(self.X_test)
+        return y_pred
+        
+    def evaluate(self, model_name):
+        if self.X_test is None or self.y_test is None:
+            raise ValueError("Data not split. Use split_data method to split data into training and testing sets.")
+        model = self.models[model_name]
+        y_pred = model.predict(self.X_test)
+        mse = mean_squared_error(self.y_test, y_pred)
+        r2 = r2_score(self.y_test, y_pred)
+        return mse, r2
+        
+    def predict(self, model_name, X):
+        model = self.models[model_name]
+        return model.predict(X)

requirements.txt

@@ -1,4 +1,6 @@
+matplotlib==3.7.0
 matplotlib==3.8.3
 pandas==1.5.3
+scikit_learn==1.2.1
 scikit_learn==1.4.1.post1
 streamlit==1.32.0