Create app.py

app.py

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+# _____________ Import Python Libraries _________________ #
+
+import streamlit as st
+import numpy as np
+import plotly.express as px
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+
+# ________________ Page Configuration Section _____________  #
+
+st.set_page_config(
+    page_title="Data Ocean",
+    page_icon= '🔥'
+)
+
+# _________________ Web Page Info Section _____________________ #
+
+st.title(":red[Data] :blue[Analytic] :orange[Portal & Machine Learning]")
+st.header(":rainbow[Explore Data With Ease]")
+
+# __________________ File Upload Section _________________ #
+
+file = st.file_uploader('Drop Your CSV, Excel', type=['csv', 'xlsx'])
+
+if file is not None:
+    try:
+        if file.name.endswith('csv'):
+            data = pd.read_csv(file)
+        elif file.name.endswith('xlsx'):
+            data = pd.read_excel(file)
+        else:
+            pass
+
+        st.dataframe(data)
+        st.success("File Successfully Uploaded" ,icon='🎉')
+
+        # ________________ Basic Info Summary Section ______________  #
+
+        st.subheader(':rainbow[Basic Information of The Dataset]',divider='violet')
+        tab1, tab2, tab3, tab4 ,tab5 , tab6 = st.tabs(['Summary', 'Top & Bottom Rows', 'Data Types', 'Columns','Missing Values','Duplicates Value'])
+
+        with tab1:
+            st.write(f'There are {data.shape[0]} Rows and {data.shape[1]} Columns in The Dataset')
+            st.subheader(':blue[Statistical Summary]')
+            st.dataframe(data.describe())
+
+        with tab2:
+            st.subheader(':gray[Top Rows]')
+            top_rows = st.slider('Number of Rows to Fetch', 1, data.shape[0], key='topslider')
+            st.dataframe(data.head(top_rows))
+
+            st.subheader(':green[Bottom Rows]')
+            bottom_rows = st.slider('Number of Rows to Fetch', 1, data.shape[0], key='bottomslider')
+            st.dataframe(data.tail(bottom_rows))
+
+        with tab3:
+            st.subheader(':orange[Data Types]')
+            st.write(data.dtypes.tolist())
+
+        with tab4:
+            st.subheader(':green[Columns]')
+            st.write(data.columns.tolist())
+
+        with tab5:
+            st.subheader(':red[Missing Values]')
+            missing_values = data.isnull().sum()
+            st.dataframe(missing_values)
+            if missing_values.sum() > 0:
+                remove_tab, fill_tab = st.tabs(['Remove Missing Values', 'Fill Missing Values'])
+
+                with remove_tab:
+                    if st.checkbox("Remove Rows with Missing Values"):
+                        data = data.dropna(inplace=True)
+                        st.success('Rows with missing values removed!', icon="🎉")
+
+                with fill_tab:
+                    replace_nulls = st.selectbox('Replace Missing Values With:', ['None', 'Mean', 'Median', 'Mode'])
+
+                    if replace_nulls != 'None':
+                        for col in data.select_dtypes(include=[np.number]):
+                            if replace_nulls == 'Mean':
+                                data[col].fillna(data[col].mean(), inplace=True)
+                            elif replace_nulls == 'Median':
+                                data[col].fillna(data[col].median(), inplace=True)
+                            elif replace_nulls == 'Mode':
+                                data[col].fillna(data[col].mode()[0], inplace=True)
+                        st.success("Missing values replaced successfully!", icon='✅')
+            else:
+                st.success("No missing values detected.", icon='🔥')
+
+        with tab6:
+            st.subheader(':green[Duplicate Values]')
+            duplicates = data.duplicated().sum()
+            if duplicates ==0:
+                st.info(f' No Duplicates Value Found',icon='🔥')
+
+            if duplicates > 0 and st.checkbox('Remove Duplicates'):
+                data = data.drop_duplicates()
+                st.success('Duplicate rows removed!', icon='🔥')
+
+
+        # __________________ Value Count Section _____________________ #
+
+        st.subheader(':rainbow[Column Value Count]',divider='green')
+        with st.expander('Value Count'):
+            col1, col2 = st.columns(2)
+            with col1:
+                column = st.selectbox('Choose Column Name', options=[None] + data.columns.tolist())
+            with col2:
+                toprows = st.number_input('Number of Top Rows', min_value=1, step=1, value=5)
+
+            if column:
+                result = data[column].value_counts().reset_index().head(toprows)
+                result.columns = [column, 'count']  
+                st.dataframe(result)
+
+                if not result.empty:
+                    fig = px.bar(data_frame=result, x=column, y='count', template='plotly_white')
+                    st.plotly_chart(fig)
+
+                    fig = px.line(data_frame=result, x=column, y='count')
+                    st.plotly_chart(fig)
+
+                    fig = px.pie(data_frame=result, names=column, values='count')
+                    st.plotly_chart(fig)
+
+        # ______________ GroupBy Section _________________________ #
+
+        st.subheader(':blue[Groupby : Simplify Your Data Analysis]',divider='violet')
+        st.write("Groupby allows you to summarize data by categories.")
+
+        with st.expander('Group By Your Columns'):
+            col1, col2, col3 = st.columns(3)
+
+            with col1:
+                groupby_cols = st.multiselect('Choose Columns to Group By', options=data.columns.tolist())
+
+            with col2:
+                operation_col = st.selectbox("Choose Column for Operation", options=data.columns.tolist())
+
+            with col3:
+                operation = st.selectbox("Choose Operation", options=['sum', 'max', 'min', 'count', 'mean', 'median'])
+
+            if groupby_cols and operation_col and operation:
+                result = data.groupby(groupby_cols).agg(newcol=(operation_col, operation)).reset_index()
+                st.dataframe(result)
+
+                st.subheader(':rainbow[Data Visualization]')
+                graph_type = st.selectbox('Choose Graph Type', options=['line', 'bar', 'scatter', 'pie', 'sunburst'])
+
+                if graph_type == 'line':
+                    x_axis = st.selectbox('X Axis', options=result.columns.tolist())
+                    y_axis = st.selectbox('Y Axis', options=result.columns.tolist())
+                    fig = px.line(data_frame=result, x=x_axis, y=y_axis)
+                    st.plotly_chart(fig)
+
+                elif graph_type == 'bar':
+                    x_axis = st.selectbox('X Axis', options=result.columns.tolist())
+                    y_axis = st.selectbox('Y Axis', options=result.columns.tolist())
+                    color = st.selectbox('Color Information', options=[None] + result.columns.tolist())
+                    fig = px.bar(data_frame=result, x=x_axis, y=y_axis, color=color)
+                    st.plotly_chart(fig)
+
+                elif graph_type == 'pie':
+                    values = st.selectbox("Numerical Values", options=result.columns.tolist())
+                    names = st.selectbox('Labels', options=result.columns.tolist())
+                    fig = px.pie(data_frame=result, names=names, values=values)
+                    st.plotly_chart(fig)
+
+                elif graph_type == 'scatter':
+                    x_axis = st.selectbox('X Axis', options=result.columns.tolist())
+                    y_axis = st.selectbox('Y Axis', options=result.columns.tolist())
+                    size = st.selectbox('Size Column', options=[None] + result.columns.tolist())
+                    color = st.selectbox('Color Information', options=[None] + result.columns.tolist())
+                    fig = px.scatter(data_frame=result, x=x_axis, y=y_axis, color=color, size=size)
+                    st.plotly_chart(fig)
+
+                elif graph_type == 'sunburst':
+                    path = st.multiselect('Path', options=result.columns.tolist())
+                    fig = px.sunburst(data_frame=result, path=path, values='newcol')
+                    st.plotly_chart(fig)
+
+        #_________________ Machine Learning_______________ #
+
+        st.subheader(":orange[Basic Machine Learning]",divider='green')
+        ml_task = st.selectbox("Select ML Task", ["None", "SVM", "Logistic Regression", "Decision Tree", "K-Nearest Neighbors"])
+
+        if ml_task != "None":
+            target_col = st.selectbox("Select Target Column", data.columns)
+            feature_cols = st.multiselect("Select Feature Columns", data.columns)
+
+            if target_col and feature_cols:
+                X = data[feature_cols]
+                y = data[target_col]
+
+                # Handle Preprocessing (Categorical and Numeric Data)
+                numeric_features = X.select_dtypes(include=['int64', 'float64']).columns
+                categorical_features = X.select_dtypes(include=['object']).columns
+
+                numeric_transformer = Pipeline(steps=[
+                    ('imputer', SimpleImputer(strategy='mean')),  # Handle missing data
+                    ('scaler', StandardScaler())  # Normalize numerical data
+                ])
+
+                categorical_transformer = Pipeline(steps=[
+                    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),  # Handle missing data
+                    ('onehot', OneHotEncoder(handle_unknown='ignore'))  # One-Hot Encode categorical features
+                ])
+
+                preprocessor = ColumnTransformer(
+                    transformers=[
+                        ('num', numeric_transformer, numeric_features),
+                        ('cat', categorical_transformer, categorical_features)
+                    ]
+                )
+
+                # Create model pipeline based on selected task
+                if ml_task == "SVM":
+                    model = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', SVC())])
+                elif ml_task == "Logistic Regression":
+                    model = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', LogisticRegression())])
+                elif ml_task == "Decision Tree":
+                    model = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', DecisionTreeClassifier())])
+                elif ml_task == "K-Nearest Neighbors":
+                    model = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', KNeighborsClassifier())])
+
+                # Split the data
+                X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+                # Train the model
+                model.fit(X_train, y_train)
+                y_pred = model.predict(X_test)
+
+                # Evaluate the model
+                accuracy = accuracy_score(y_test, y_pred)
+                st.write(f"Model Accuracy: {accuracy * 100:.2f}%")
+                
+    except Exception as e:
+        st.error(f"An error occurred: {e}")