analysis content initial

pages/04_Case Study:_Gaming_Clips.py

@@ -25,6 +25,102 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 
+def plot_chart(data, chart_type, x_var, y_var, z_var=None, show_regression_line=False, show_r_squared=False):
+    scatter_marker_color = 'green'
+    regression_line_color = 'red'
+    if chart_type == "line":
+        fig = px.line(data, x=x_var, y=y_var)
+
+    elif chart_type == "bar":
+        fig = px.bar(data, x=x_var, y=y_var)
+
+    elif chart_type == "scatter":
+        fig = px.scatter(data, x=x_var, y=y_var, color_discrete_sequence=[scatter_marker_color])
+
+        if show_regression_line and x_var != 'Date':
+            X = data[x_var].values.reshape(-1, 1)
+            y = data[y_var].values.reshape(-1, 1)
+            model = LinearRegression().fit(X, y)
+            y_pred = model.predict(X)
+            r_squared = r2_score(y, y_pred)  # Calculate R-squared value
+
+            fig.add_trace(
+                go.Scatter(x=data[x_var], y=y_pred[:, 0], mode='lines', name='Regression Line', line=dict(color=regression_line_color))
+            )
+
+            # Add R-squared value as a text annotation
+            fig.add_annotation(
+                x=data[x_var].max(),
+                y=y_pred[-1, 0],
+                text=f"R-squared: {r_squared:.4f}",
+                showarrow=False,
+                font=dict(size=14),
+                bgcolor='rgba(255, 255, 255, 0.8)',
+                bordercolor='black',
+                borderwidth=1,
+                borderpad=4
+            )
+
+    elif chart_type == "heatmap":
+        fig = px.imshow(data, color_continuous_scale='Inferno')
+
+    elif chart_type == "scatter_3d":
+        if z_var is not None:
+            fig = px.scatter_3d(data, x=x_var, y=y_var, z=z_var, color=data.columns[0])
+        else:
+            st.warning("Please select Z variable for 3D line plot.")
+            return
+
+    elif chart_type == "line_3d":
+        if z_var is not None:
+            fig = go.Figure(data=[go.Scatter3d(x=data[x_var], y=data[y_var], z=data[z_var], mode='lines')])
+            fig.update_layout(scene=dict(xaxis_title=x_var, yaxis_title=y_var, zaxis_title=z_var))  # Set the axis name
+        else:
+            st.warning("Please select Z variable for 3D line plot.")
+            return
+
+    elif chart_type == "surface_3d":
+        if z_var is not None:
+            fig = go.Figure(data=[go.Surface(z=data.values)])
+            fig.update_layout(scene=dict(xaxis_title=x_var, yaxis_title=y_var, zaxis_title=z_var))  # Set the axis name
+        else:
+            st.warning("Please select Z variable for 3D line plot.")
+            return
+
+    elif chart_type == "radar":
+        fig = go.Figure()
+        for col in data.columns[1:]:
+            fig.add_trace(go.Scatterpolar(r=data[col], theta=data[x_var], mode='lines', name=col))
+        fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[data[data.columns[1:]].min().min(), data[data.columns[1:]].max().max()])))
+
+    st.plotly_chart(fig)
+
+def plot_radar_chart(data, columns):
+    df = data[columns]
+    fig = go.Figure()
+
+    for i in range(len(df)):
+        date_label = data.loc[i, 'Date']
+        fig.add_trace(go.Scatterpolar(
+            r=df.loc[i].values,
+            theta=df.columns,
+            fill='toself',
+            name=date_label
+        ))
+
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, df.max().max()]
+            )
+        ),
+        showlegend=True
+    )
+
+    st.plotly_chart(fig)
+
+
 
 # Set page title
 st.set_page_config(page_title="Case Study: Gaming Clips - Tiktok Analytics Dashboard", page_icon = "📊", layout = "centered", initial_sidebar_state = "auto")
@@ -124,106 +220,13 @@ elif selected == "Uploaded Datasets":
         mime='text/csv',
     )
 elif selected == "Analysis":
-    def plot_chart(data, chart_type, x_var, y_var, z_var=None, show_regression_line=False, show_r_squared=False):
-        scatter_marker_color = 'green'
-        regression_line_color = 'red'
-        if chart_type == "line":
-            fig = px.line(data, x=x_var, y=y_var)
-
-        elif chart_type == "bar":
-            fig = px.bar(data, x=x_var, y=y_var)
-
-        elif chart_type == "scatter":
-            fig = px.scatter(data, x=x_var, y=y_var, color_discrete_sequence=[scatter_marker_color])
-
-            if show_regression_line and x_var != 'Date':
-                X = data[x_var].values.reshape(-1, 1)
-                y = data[y_var].values.reshape(-1, 1)
-                model = LinearRegression().fit(X, y)
-                y_pred = model.predict(X)
-                r_squared = r2_score(y, y_pred)  # Calculate R-squared value
-
-                fig.add_trace(
-                    go.Scatter(x=data[x_var], y=y_pred[:, 0], mode='lines', name='Regression Line', line=dict(color=regression_line_color))
-                )
-
-                # Add R-squared value as a text annotation
-                fig.add_annotation(
-                    x=data[x_var].max(),
-                    y=y_pred[-1, 0],
-                    text=f"R-squared: {r_squared:.4f}",
-                    showarrow=False,
-                    font=dict(size=14),
-                    bgcolor='rgba(255, 255, 255, 0.8)',
-                    bordercolor='black',
-                    borderwidth=1,
-                    borderpad=4
-                )
-
-        elif chart_type == "heatmap":
-            fig = px.imshow(data, color_continuous_scale='Inferno')
-
-        elif chart_type == "scatter_3d":
-            if z_var is not None:
-                fig = px.scatter_3d(data, x=x_var, y=y_var, z=z_var, color=data.columns[0])
-            else:
-                st.warning("Please select Z variable for 3D line plot.")
-                return
-
-        elif chart_type == "line_3d":
-            if z_var is not None:
-                fig = go.Figure(data=[go.Scatter3d(x=data[x_var], y=data[y_var], z=data[z_var], mode='lines')])
-                fig.update_layout(scene=dict(xaxis_title=x_var, yaxis_title=y_var, zaxis_title=z_var))  # Set the axis name
-            else:
-                st.warning("Please select Z variable for 3D line plot.")
-                return
-
-        elif chart_type == "surface_3d":
-            if z_var is not None:
-                fig = go.Figure(data=[go.Surface(z=data.values)])
-                fig.update_layout(scene=dict(xaxis_title=x_var, yaxis_title=y_var, zaxis_title=z_var))  # Set the axis name
-            else:
-                st.warning("Please select Z variable for 3D line plot.")
-                return
-
-        elif chart_type == "radar":
-            fig = go.Figure()
-            for col in data.columns[1:]:
-                fig.add_trace(go.Scatterpolar(r=data[col], theta=data[x_var], mode='lines', name=col))
-            fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[data[data.columns[1:]].min().min(), data[data.columns[1:]].max().max()])))
-
-        st.plotly_chart(fig)
-
-    def plot_radar_chart(data, columns):
-        df = data[columns]
-        fig = go.Figure()
-
-        for i in range(len(df)):
-            date_label = data.loc[i, 'Date']
-            fig.add_trace(go.Scatterpolar(
-                r=df.loc[i].values,
-                theta=df.columns,
-                fill='toself',
-                name=date_label
-            ))
-
-        fig.update_layout(
-            polar=dict(
-                radialaxis=dict(
-                    visible=True,
-                    range=[0, df.max().max()]
-                )
-            ),
-            showlegend=True
-        )
-
-        st.plotly_chart(fig)
     st.subheader("Analysis")
     taba, tabb, tabc = st.tabs(["Overview", "Content", "Followers"])
     with taba:
         st.write("**Overview**")
 
         data = pd.read_excel('gaming/Last 7 days.xlsx')
+        st.write(data)
 
         x_var = st.sidebar.selectbox("Select X variable for Last 7 days", data.columns)
         y_var = st.sidebar.selectbox("Select Y variable for Last 7 days", data.columns)
@@ -274,6 +277,406 @@ elif selected == "Analysis":
 
     with tabb:
         st.write("**Content**")
+        def extract_hashtags(title):
+            if isinstance(title, (str, bytes)):
+                return re.findall(r'#\w+', title)
+            else:
+                return []  # or raise an exception or perform other error handling
+                
+        # Remove emojis and hashtags from the title
+        def clean_title(title):
+            #title_without_emojis = emoji.get_emoji_regexp().sub(u'', title)
+            title_without_hashtags = re.sub(r'#\w+', '', title)
+            return title_without_hashtags.strip()
+
+        data = pd.read_excel('gaming/Video Posts.xlsx')
+
+        data['Video title'] = data['Video title'].fillna("")  # Fill missing values with empty string
+        data['Video title'] = data['Video title'].astype(str)  # Convert to string type
+
+        data['Hashtags'] = data['Video title'].apply(extract_hashtags)
+        #data['Emojis'] = data['Video title'].apply(extract_emojis)
+        data['Cleaned_title'] = data['Video title'].apply(clean_title)
+        # Add a new column to store the hashtag count
+        data['Hashtag_count'] = data['Hashtags'].apply(len)
+        # Convert the 'post time' column to datetime format
+        data['Post time'] = pd.to_datetime(data['Post time'])
+        # Create new columns for 'weekday', 'hour' and 'minute'
+        data['weekday_posted'] = data['Post time'].dt.day_name()
+        data['hour_posted'] = data['Post time'].dt.hour
+        data['min_posted'] = data['Post time'].dt.minute
+        st.write(data)
+        options = ["Summary", "Total views", "Total shares", "Total likes", "Total comments", "Number of Hashtags", "Hashtag Performance"]
+        selected_feature = st.selectbox(label="Select feature", options=options, index=0)
+                
+        if selected_feature == "Summary":
+            x_var = st.sidebar.selectbox("Select X variable", data.columns)
+            y_var = st.sidebar.selectbox("Select Y variable", data.columns)
+            show_regression_line = False
+    
+            z_var_options = ["None"] + list(data.columns)
+            z_var = st.sidebar.selectbox("Select Z variable for 3D charts (if applicable)", z_var_options)
+            
+
+            tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Line", "Bar", "Scatterplot", "Heatmap", 
+                                                                    "3D Scatterplot", "3D Lineplot", "3D Surfaceplot"])
+            with tab1:
+                st.write("Lineplot")
+                plot_chart(data, "line", x_var, y_var)
+
+            with tab2:
+                st.write("Barplot")
+                plot_chart(data, "bar", x_var, y_var)
+
+            with tab3:
+                st.write("Scatterplot")
+                show_regression_line = st.checkbox("Show regression line for scatterplot")
+                plot_chart(data, "scatter", x_var, y_var, show_regression_line=show_regression_line)
+
+            with tab4:
+                st.write("Heatmap")
+                plot_chart(data, "heatmap", x_var, y_var) 
+
+            with tab5:
+                st.write("3D Scatterplot")
+                if z_var != "None":
+                    plot_chart(data, "scatter_3d", x_var, y_var, z_var)    
+
+            with tab6:
+                st.write("3D Lineplot")
+                if z_var != "None":
+                    plot_chart(data, "line_3d", x_var, y_var, z_var)
+
+            with tab7:
+                st.write("3D Surfaceplot")
+                if z_var != "None":
+                    plot_chart(data, "surface_3d", x_var, y_var, z_var)
+
+
+                
+        elif selected_feature == "Total views":
+            data = data.sort_values(by='Total views', ascending=True)
+            fig = px.bar(data, x='Total views', y='Cleaned_title', title='Views of trending videos for the week',
+                    color_discrete_sequence=px.colors.qualitative.Alphabet, hover_data={'Total views': ':.2f'})
+            st.plotly_chart(fig)
+        elif selected_feature == "Total shares":
+            data = data.sort_values(by='Total shares\xa0', ascending=True)
+            fig = px.bar(data, x='Total shares\xa0', y='Cleaned_title', title='Shares of trending videos for the week',
+                    color_discrete_sequence=px.colors.qualitative.Set1, hover_data={'Total shares\xa0': ':.2f'})
+            st.plotly_chart(fig)
+        elif selected_feature == "Total likes":
+            data = data.sort_values(by='Total likes', ascending=True)
+            fig = px.bar(data, x='Total likes', y='Cleaned_title', title='Likes of trending videos for the week',
+                    color_discrete_sequence=px.colors.qualitative.Antique, hover_data={'Total likes': ':.2f'})
+            st.plotly_chart(fig)
+        elif selected_feature == "Total comments":
+            data = data.sort_values(by='Total comments', ascending=True)
+            fig = px.bar(data, x='Total comments', y='Cleaned_title', title='Comments of trending videos for the week',
+                    color_discrete_sequence=px.colors.qualitative.Vivid, hover_data={'Total comments': ':.2f'})
+            st.plotly_chart(fig)
+        elif selected_feature == "Number of Hashtags":
+            # Count the occurrences of each hashtag
+            hashtag_counts = Counter(hashtag for hashtags in data['Hashtags'] for hashtag in hashtags)
+
+            # Get the top N most common hashtags
+            N = 10
+            top_hashtags = hashtag_counts.most_common(N)
+
+            # Display the top hashtags
+            print(f"Top {N} hashtags:")
+            for hashtag, count in top_hashtags:
+                print(f"{hashtag}: {count}")
+
+            # Visualize the results with a Plotly bar chart
+            fig = go.Figure(go.Bar(
+                x=[t[0] for t in top_hashtags],
+                y=[t[1] for t in top_hashtags],
+                text=[t[1] for t in top_hashtags],
+                textposition='auto',
+                marker_color='rgba(58, 71, 80, 0.6)',
+                opacity=0.8
+            ))
+
+            fig.update_layout(
+                title=f'Top {N} Hashtags',
+                xaxis_title='Hashtags',
+                yaxis_title='Count',
+                xaxis_tickangle=-45
+            )
+
+            st.plotly_chart(fig)
+
+            tab1, tab2, tab3, tab4 = st.tabs(["vs Views", "vs Shares", "vs Likes", "vs Comments"])
+            with tab1:
+                fig = px.scatter(data, x='Hashtag_count', y='Total views', hover_data=['Cleaned_title'])
+                fig.update_layout(
+                    title='Hashtag Count vs. Views',
+                    xaxis_title='Hashtag Count',
+                    yaxis_title='Views'
+                )
+                st.plotly_chart(fig)
+            with tab2:
+                fig = px.scatter(data, x='Hashtag_count', y='Total shares\xa0', hover_data=['Cleaned_title'])
+                fig.update_layout(
+                    title='Hashtag Count vs. Shares',
+                    xaxis_title='Hashtag Count',
+                    yaxis_title='Shares'
+                )
+                st.plotly_chart(fig)
+            with tab3:
+                fig = px.scatter(data, x='Hashtag_count', y='Total likes', hover_data=['Cleaned_title'])
+                fig.update_layout(
+                    title='Hashtag Count vs. Likes',
+                    xaxis_title='Hashtag Count',
+                    yaxis_title='Likes'
+                )
+                st.plotly_chart(fig)
+            with tab4:
+                fig = px.scatter(data, x='Hashtag_count', y='Total comments', hover_data=['Cleaned_title'])
+                fig.update_layout(
+                    title='Hashtag Count vs. Comments',
+                    xaxis_title='Hashtag Count',
+                    yaxis_title='Comments'
+                )
+                st.plotly_chart(fig)
+        elif selected_feature == "Hashtag Performance":
+            # Tokenize hashtags and create a list of unique hashtags
+            tokenized_hashtags = data["Hashtags"].tolist()
+            unique_hashtags = list(set([tag for tags in tokenized_hashtags for tag in tags]))
+            # Train a word2vec model
+            model = Word2Vec(tokenized_hashtags, size=50, window=5, min_count=1, workers=4)
+
+            # Create a hashtag vector dictionary
+            hashtag_vectors = {tag: model.wv[tag] for tag in unique_hashtags}
+            st.subheader("Explaining the concept of hashtag performance scores and cosine similarity scores")
+            st.markdown(
+                        """
+                    So how are the **performance scores** calculated for each feature? 
+
+                    In each line, the code goes through each unique hashtag and selects all videos that use that hashtag. It then calculates the mean of the respective performance metric (views, shares, likes, or comments) for those videos.
+
+                    This gives an average performance score for each hashtag, which can be used as an indication of how well videos with that hashtag tend to perform on average. However, this is a simplistic metric and there may be other factors influencing the performance of a video. It's also worth noting that the mean is sensitive to extreme values, so a few very popular or unpopular videos could skew the average performance for a given hashtag.
+                    """)
+
+            st.markdown("""
+                    How about **cosine similarity**?
+
+                    Cosine similarity is a metric used to measure how similar two vectors are, irrespective of their size. In the context of Natural Language Processing (NLP), and in this case, it's used to measure the semantic similarity between two hashtags based on their embeddings (vectors) generated by the Word2Vec model.
+
+                    In simple terms, it measures the cosine of the angle between two vectors. If the vectors are identical, the angle is 0, so the cosine is 1, indicating perfect similarity. If the vectors are orthogonal (i.e., the angle between them is 90 degrees), they're considered not similar, and the cosine similarity is 0. If the vectors point in opposite directions (i.e., the angle is 180 degrees), the cosine similarity is -1, indicating that they're diametrically dissimilar.
+                    """)
+            # Calculate the average performance of each hashtag - views
+            hashtag_performance_views = {tag: data[data["Hashtags"].apply(lambda x: tag in x)]["Total views"].mean() for tag in unique_hashtags}
+
+            # Calculate the average performance of each hashtag - shares
+            hashtag_performance_shares = {tag: data[data["Hashtags"].apply(lambda x: tag in x)]["Total shares\xa0"].mean() for tag in unique_hashtags}
+
+            # Calculate the average performance of each hashtag - likes
+            hashtag_performance_likes = {tag: data[data["Hashtags"].apply(lambda x: tag in x)]["Total likes"].mean() for tag in unique_hashtags}
+
+            # Calculate the average performance of each hashtag - comments
+            hashtag_performance_comments = {tag: data[data["Hashtags"].apply(lambda x: tag in x)]["Total comments"].mean() for tag in unique_hashtags}
+
+            # Calculate the similarity between hashtags
+            similarity_matrix = cosine_similarity(list(hashtag_vectors.values()))
+
+            # Convert the similarity matrix into a DataFrame
+            similarity_df = pd.DataFrame(similarity_matrix, index=unique_hashtags, columns=unique_hashtags)
+
+            # Convert the performance dictionaries into DataFrames
+            perf_views_df = pd.DataFrame(list(hashtag_performance_views.items()), columns=["hashtag", "views"])
+            perf_shares_df = pd.DataFrame(list(hashtag_performance_shares.items()), columns=["hashtag", "shares"])
+            perf_likes_df = pd.DataFrame(list(hashtag_performance_likes.items()), columns=["hashtag", "likes"])
+            perf_comments_df = pd.DataFrame(list(hashtag_performance_comments.items()), columns=["hashtag", "comments"])
+
+            # Merge the performance DataFrames into a single DataFrame
+            perf_df = pd.merge(perf_views_df, perf_shares_df, on="hashtag")
+            perf_df = pd.merge(perf_df, perf_likes_df, on="hashtag")
+            perf_df = pd.merge(perf_df, perf_comments_df, on="hashtag")
+
+            # Convert the similarity matrix into a 1D series
+            similarity_series = similarity_df.unstack()
+
+            # Rename the series index
+            similarity_series.index.rename(["hashtag1", "hashtag2"], inplace=True)
+
+            # Convert the series into a DataFrame
+            similarity_df = similarity_series.to_frame("similarity").reset_index()
+
+            # Merge the similarity DataFrame with the performance DataFrame
+            merged_df = pd.merge(similarity_df, perf_df, left_on="hashtag1", right_on="hashtag")
+
+            # Calculate the correlation between hashtag similarity and performance
+            correlation = merged_df[["similarity", "views", "shares", "likes", "comments"]].corr()
+            st.subheader("Correlation matrix between hashtag cosine similarity values and performance values")
+            #st.write(correlation)
+            # Create a heatmap
+            plt.figure(figsize=(10, 8))
+            sns.heatmap(correlation, annot=True, fmt=".2f", cmap='coolwarm', cbar=True)
+
+            # Show the plot in Streamlit
+            st.pyplot(plt)
+            # Rename the 'value' columns to make them unique
+            df1 = pd.DataFrame(list(hashtag_performance_views.items()), columns=["hashtag", "value"])
+            df2 = pd.DataFrame(list(hashtag_performance_shares.items()), columns=["hashtag", "value"])
+            df3 = pd.DataFrame(list(hashtag_performance_likes.items()), columns=["hashtag", "value"])
+            df4 = pd.DataFrame(list(hashtag_performance_comments.items()), columns=["hashtag", "value"])
+
+            df1.rename(columns={'value': 'value_views'}, inplace=True)
+            df2.rename(columns={'value': 'value_shares'}, inplace=True)
+            df3.rename(columns={'value': 'value_likes'}, inplace=True)
+            df4.rename(columns={'value': 'value_comments'}, inplace=True)
+
+            # Merge the DataFrames on the 'hashtag' column
+            merged_df = df1.merge(df2, on='hashtag').merge(df3, on='hashtag').merge(df4, on='hashtag')
+
+            st.subheader("Hashtag Performance Scores based on Views, Shares, Likes and Comments - Calculated using average of all videos' metrics containing a particular hashtag")
+            st.write(merged_df)
+
+            # Create a pair plot with regression lines
+            st.write("**Pair Plots**")
+            sns.pairplot(merged_df, kind="reg", diag_kind="kde")
+            st.pyplot(plt)
+
+            # Split the data into train and test sets
+            X = merged_df[["value_shares", "value_likes", "value_comments"]]
+            y = merged_df["value_views"]
+            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+            # Fit the models on the training data
+            lr_model = LinearRegression()
+            lr_model.fit(X_train, y_train)
+
+            rf_model = RandomForestRegressor()
+            rf_model.fit(X_train, y_train)
+
+            xgb_model = XGBRegressor()
+            xgb_model.fit(X_train, y_train)
+
+            # Make predictions on the testing data using the trained models
+            lr_pred = lr_model.predict(X_test)
+            rf_pred = rf_model.predict(X_test)
+            xgb_pred = xgb_model.predict(X_test)
+
+            # Calculate the evaluation metrics for each model
+            st.write("**Training regression models to predict value_views using value_shares, value_likes and value_comments**")
+            models = ["Linear Regression", "Random Forest", "XGBoost"]
+            predictions = [lr_pred, rf_pred, xgb_pred]
+
+            # Initialize a list to hold the model metrics
+            model_metrics = []
+
+            for model, pred in zip(models, predictions):
+                mse = mean_squared_error(y_test, pred)
+                mae = mean_absolute_error(y_test, pred)
+                r2 = r2_score(y_test, pred)
+    
+                # Append a dictionary of the metrics to the list
+                model_metrics.append({"Model": model, "Mean Squared Error": mse, "Mean Absolute Error": mae, "R^2 Score": r2})
+                    
+            # Convert the list of dictionaries into a DataFrame
+            metrics_df = pd.DataFrame(model_metrics)
+
+            # Display the DataFrame in Streamlit
+            st.write(metrics_df)
+
+
+            x_var = st.sidebar.selectbox("Select X variable", merged_df.columns)
+            y_var = st.sidebar.selectbox("Select Y variable", merged_df.columns)
+            show_regression_line = False
+    
+            z_var_options = ["None"] + list(merged_df.columns)
+            z_var = st.sidebar.selectbox("Select Z variable for 3D charts (if applicable)", z_var_options)
+            
+            st.subheader("Various plots to represent performance scores for views, shares, likes and comments")
+            tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Line", "Bar", "Scatterplot", "Heatmap", 
+                                                            "3D Scatterplot", "3D Lineplot", "3D Surfaceplot"])
+            with tab1:
+                st.write("Lineplot")
+                plot_chart(merged_df, "line", x_var, y_var)
+
+            with tab2:
+                st.write("Barplot")
+                plot_chart(merged_df, "bar", x_var, y_var)
+
+            with tab3:
+                st.write("Scatterplot")
+                show_regression_line = st.checkbox("Show regression line")
+                plot_chart(merged_df, "scatter", x_var, y_var, show_regression_line=show_regression_line)
+
+            with tab4:
+                st.write("Heatmap")
+                plot_chart(merged_df, "heatmap", x_var, y_var) 
+
+            with tab5:
+                st.write("3D Scatterplot")
+                if z_var != "None":
+                    plot_chart(merged_df, "scatter_3d", x_var, y_var, z_var)    
+
+            with tab6:
+                st.write("3D Lineplot")
+                if z_var != "None":
+                    plot_chart(merged_df, "line_3d", x_var, y_var, z_var)
+
+            with tab7:
+                st.write("3D Surfaceplot")
+                if z_var != "None":
+                    plot_chart(merged_df, "surface_3d", x_var, y_var, z_var)
+
+            tab1, tab2, tab3, tab4 = st.tabs(["vs Views", "vs Shares", "vs Likes", "vs Comments"])
+            with tab1:
+                st.subheader("Hashtag Performance - Views:")
+                # Sort the DataFrame by the 'value' column in descending order
+                sorted_df1 = df1.sort_values(by="value_views", ascending=False)
+                st.write(sorted_df1)
+                # Highlight specific bars (use 'rgba' values for transparency)
+                highlighted_bars = ['#fyp', '#tiktok', '#foryou', '#trending', '#viral']
+                sorted_df1['color'] = sorted_df1['hashtag'].apply(lambda x: 'black' if x in highlighted_bars else 'red')
+                fig = px.bar(sorted_df1, x='hashtag', y='value_views', title='Hashtag performance for the week',
+                color='color', color_discrete_map='identity', hover_data={'value_views': ':.2f'})
+
+                fig.update_layout(title='Hashtag performance for the week - Views', xaxis_title='Hashtag', yaxis_title='Value')
+                st.plotly_chart(fig)
+                        
+            with tab2:
+                st.subheader("Hashtag Performance - Shares:")
+                # Sort the DataFrame by the 'value' column in descending order
+                sorted_df2 = df2.sort_values(by="value_shares", ascending=False)
+                st.write(sorted_df2)
+                # Highlight specific bars (use 'rgba' values for transparency)
+                highlighted_bars = ['#fyp', '#tiktok', '#foryou', '#trending', '#viral']
+                sorted_df2['color'] = sorted_df2['hashtag'].apply(lambda x: 'black' if x in highlighted_bars else 'blue')
+                fig = px.bar(sorted_df2, x='hashtag', y='value_shares', title='Hashtag performance for the week',
+                color='color', color_discrete_map='identity', hover_data={'value_shares': ':.2f'})
+
+                fig.update_layout(title='Hashtag performance for the week - Shares', xaxis_title='Hashtag', yaxis_title='Value')
+                st.plotly_chart(fig)
+            with tab3:
+                st.subheader("Hashtag Performance - Likes:")
+                # Sort the DataFrame by the 'value' column in descending order
+                sorted_df3 = df3.sort_values(by="value_likes", ascending=False)
+                st.write(sorted_df3)
+                # Highlight specific bars (use 'rgba' values for transparency)
+                highlighted_bars = ['#fyp', '#tiktok', '#foryou', '#trending', '#viral']
+                sorted_df3['color'] = sorted_df3['hashtag'].apply(lambda x: 'black' if x in highlighted_bars else 'green')
+                fig = px.bar(sorted_df3, x='hashtag', y='value_likes', title='Hashtag performance for the week',
+                color='color', color_discrete_map='identity', hover_data={'value_likes': ':.2f'})
+
+                fig.update_layout(title='Hashtag performance for the week - Likes', xaxis_title='Hashtag', yaxis_title='Value')
+                st.plotly_chart(fig)
+            with tab4:
+                st.subheader("Hashtag Performance - Comments:")
+                # Sort the DataFrame by the 'value' column in descending order
+                sorted_df4 = df4.sort_values(by="value_comments", ascending=False)
+                st.write(sorted_df4)
+                # Highlight specific bars (use 'rgba' values for transparency)
+                highlighted_bars = ['#fyp', '#tiktok', '#foryou', '#trending', '#viral']
+                sorted_df4['color'] = sorted_df4['hashtag'].apply(lambda x: 'black' if x in highlighted_bars else 'orange')
+                fig = px.bar(sorted_df4, x='hashtag', y='value_comments', title='Hashtag performance for the week',
+                color='color', color_discrete_map='identity', hover_data={'value_comments': ':.2f'})
+
+                fig.update_layout(title='Hashtag performance for the week - Comments', xaxis_title='Hashtag', yaxis_title='Value')
+                st.plotly_chart(fig)
 
     with tabc:
         st.write("**Followers**")