diff --git a/adas-data-analysis-dashboards/app.py b/adas-data-analysis-dashboards/app.py
new file mode 100644
index 0000000..1efdcb2
--- /dev/null
+++ b/adas-data-analysis-dashboards/app.py
@@ -0,0 +1,439 @@
+from h2o_wave import main, app, Q, ui, on, run_on, data
+from typing import Optional, List
+import pandas as pd
+import plotly.express as px
+import io
+import base64
+import matplotlib.pyplot as plt
+from plotly import io as pio
+import plotly.figure_factory as ff
+
+
+df = pd.read_csv('data/iraste_nxt_cas.csv')
+df1 = pd.read_csv('data/iraste_nxt_casdms.csv')
+df = pd.concat([df, df1], axis = 0)
+df = df.drop_duplicates()
+df = df.dropna()
+df = df.sample(frac=0.01, random_state=42)
+
+
+
+
+# Use for page cards that should be removed when navigating away.
+# For pages that should be always present on screen use q.page[key] = ...
+def add_card(q, name, card) -> None:
+    q.client.cards.add(name)
+    q.page[name] = card
+
+
+# Remove all the cards related to navigation.
+def clear_cards(q, ignore: Optional[List[str]] = []) -> None:
+    if not q.client.cards:
+        return
+
+    for name in q.client.cards.copy():
+        if name not in ignore:
+            del q.page[name]
+            q.client.cards.remove(name)
+
+
+@on('#intro')
+async def page_intro(q: Q):
+    q.page['sidebar'].value = '#intro'
+    clear_cards(q)  # When routing, drop all the cards except of the main ones (header, sidebar, meta).
+    # Create a heatmap of alert occurrences
+    fig = px.density_mapbox(df, lat='Lat', lon='Long', radius=10, zoom=5, mapbox_style='carto-positron',
+                        title='Spatial Distribution of Alert Occurrences')
+
+# Update map layout
+    fig.update_layout(mapbox_center={'lat': df['Lat'].mean(), 'lon': df['Long'].mean()})
+    
+
+    config = {
+        'scrollZoom': True,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'spatia1', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='1000px', width='1300px')]))
+    
+@on('#data-frame-analysis')
+async def page_df(q: Q):
+    q.page['sidebar'].value = '#data-frame-analysis'
+    clear_cards(q)
+    
+    # Add description dictionary
+    descriptions = {
+        'cas_ldw': "Lane Departure Warning (LDW) - A system that alerts the driver when the vehicle is unintentionally drifting out of its lane without a turn signal.",
+        'cas_hmw': "Headway Monitoring and Warning (HMW) - A system that monitors the distance between the driver's vehicle and the vehicle in front and alerts the driver if the distance becomes dangerously short.",
+        'hard_brake': "Hard Brake - An event recorded when the driver applies the brakes abruptly and significantly, often indicating an emergency or sudden deceleration.",
+        'cas_pcw': "Pedestrian Collision Warning (PCW) - A system that detects pedestrians in the vehicle's path and alerts the driver to avoid collisions.",
+        'cas_fcw': "Forward Collision Warning (FCW) - A system that detects vehicles or obstacles in the vehicle's path and alerts the driver to potential collisions.",
+        'dms_distracted': "Driver Monitoring System (DMS) - Distracted - An event recorded when the driver is distracted, such as by using a mobile phone or engaging in other activities instead of focusing on driving.",
+        'dms_noseatbelt': "Driver Monitoring System (DMS) - No Seatbelt - An event recorded when the driver is not wearing a seatbelt.",
+        'dms_smoking': "Driver Monitoring System (DMS) - Smoking - An event recorded when the driver is smoking while driving."
+    }
+    
+    table_rows = []
+    df_temp = df.copy()
+    df_temp['Date'] = df_temp['Date'].astype(str)  # Convert datetime to string for display
+    df_temp['Speed'] = df_temp['Speed'].astype(str)  # Convert speed to string for display
+    for index, row in df_temp.iterrows():
+        if index == 10000:
+            break
+        table_rows.append(ui.table_row(
+            name=row['Date'],
+            cells=[row['Date'], row['Alert'], descriptions.get(row['Alert'], ''), row['Speed']]  # Adjust these indices based on your CSV columns
+        ))
+        
+    add_card(q, 'table', ui.form_card(box='vertical', items=[ui.table(
+        name='table',
+        downloadable=True,
+        resettable=True,
+        groupable=True,
+        columns=[
+            ui.table_column(name='Date', label='Date', searchable=True, min_width='200'),
+            ui.table_column(name='Alert', label='Alert', filterable=True, min_width='200', cell_type=ui.tag_table_cell_type(name='tags', tags=[
+                ui.tag(label='RUNNING', color='#D2E3F8'),
+                ui.tag(label='DONE', color='$red'),
+                ui.tag(label='SUCCESS', color='$mint'),
+            ])),
+            ui.table_column(name='Description', label='Description', searchable=True, min_width='800'),
+            ui.table_column(name='Speed', label='Speed', searchable=True, min_width='200'),
+        ],
+        events=[''],
+        rows=table_rows)]
+    ))
+
+
+@on('table')
+async def handle_table_click(q: Q):
+    table_rows = []
+    for index, row in df.iterrows():
+        table_rows.append(ui.table_row(
+            name=row['Date'],
+            cells=[row['Date'], row['Alert'], row['Speed']]  # Adjust these indices based on your CSV columns
+        ))
+    print(q.args.table)
+    if q.args.table:
+        q.client.selected_actor = q.args.table[0]
+        q.args['#'] = 'data-frame-analysis'
+        await page_df(q)
+
+@on('#alert-frequency-analysis')
+async def pageca(q: Q):
+    print('Handling page4')
+
+    q.page['sidebar'].value = '#alert-frequency-analysis'
+    clear_cards(q)
+
+
+    # Assuming df is your DataFrame containing the dataset
+
+    # Convert 'Date' column to datetime format
+    df['Date'] = pd.to_datetime(df['Date'])
+
+    # Extract day of the week and hour of the day from the 'Date' column
+    df['DayOfWeek'] = df['Date'].dt.day_name()
+    df['HourOfDay'] = df['Date'].dt.hour
+
+    # Alert Frequency Analysis by Day of Week
+    fig1 = px.histogram(df, x='DayOfWeek', color='Alert', title='Alert Frequency by Day of Week')
+    fig1.update_layout(xaxis={'categoryorder':'total descending'},width = 1300)
+
+    config = {
+        'scrollZoom': False,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig1, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'bar1', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='650px', width='1300px')]))
+
+    fig3 = px.scatter(df, x='Speed', color='Alert', title='Alert Frequency Comparison Across Different Vehicles')
+    fig3.update_layout(xaxis_title='Speed', yaxis_title='Alert Frequency', width = 1300)
+    config = {
+        'scrollZoom': False,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig3, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'bar2', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='650px', width='1300px')]))
+   
+
+@on('#speed-analysis')
+async def page_ind(q: Q):
+    q.page['sidebar'].value = '#speed-analysis'
+    clear_cards(q)
+    
+    # Convert 'Speed' column to float
+    df['Speed'] = df['Speed'].astype(float)
+    
+    # Sort DataFrame by 'Time'
+    df_sorted = df.sort_values(by='Time')
+    df_sorted['Time'] = pd.to_datetime(df_sorted['Time'], errors='coerce')
+    
+    # Create a new column for speed category
+    def categorize_speed(speed):
+        if speed < 60:
+            return 'Low'
+        elif 60 <= speed < 80:
+            return 'Medium'
+        else:
+            return 'High'
+    
+    df_sorted['Speed_Category'] = df_sorted['Speed'].apply(categorize_speed)
+    
+    # Scatter plot of Speed vs. Time with Alert Events
+    fig1 = px.scatter(df_sorted, x='Time', y='Speed', color='Alert', title='Speed vs. Time with Alert Events')
+    fig1.update_layout(xaxis_title='Time', yaxis_title='Speed')
+    
+    # Convert plot to HTML
+    html1 = pio.to_html(fig1, validate=False, include_plotlyjs='cdn', config={'scrollZoom': False, 'showLink': False, 'displayModeBar': False})
+    
+    # Add the plot to a card
+    add_card(q, 'speed1', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html1, height='650px', width='1500px')
+    ]))
+    
+    # Histogram of Speed Distribution
+    fig2 = px.histogram(df_sorted, x='Speed', nbins=20, title='Distribution of Speed')
+    fig2.update_layout(xaxis_title='Speed', yaxis_title='Frequency')
+    
+    # Convert plot to HTML
+    html2 = pio.to_html(fig2, validate=False, include_plotlyjs='cdn', config={'scrollZoom': False, 'showLink': False, 'displayModeBar': False})
+    
+    # Add the plot to a card
+    add_card(q, 'speed2', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html2, height='650px', width='1500px')
+    ]))
+    grouped_data = df_sorted.groupby(['Speed_Category', 'Alert']).size().reset_index(name='Count')
+    
+    # Create grouped bar plot
+    fig3 = px.bar(grouped_data, x='Speed_Category', y='Count', color='Alert', barmode='group',
+                  title='Alerts Count by Speed Category')
+    fig3.update_layout(xaxis_title='Speed Category', yaxis_title='Count')
+    
+    # Convert plot to HTML
+    html3 = pio.to_html(fig3, validate=False, include_plotlyjs='cdn', config={'scrollZoom': False, 'showLink': False, 'displayModeBar': False})
+    
+    # Add the plot to a card
+    add_card(q, 'speed3', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html3, height='650px', width='1500px')
+    ]))
+
+    
+
+    
+
+@on('#correlation-analysis')
+async def page_temporal(q: Q):
+    q.page['sidebar'].value = '#correlation-analysis'
+    clear_cards(q)  # When routing, drop all the cards except of the main ones (header, sidebar, meta).
+    
+    df1 = df.copy()
+    
+    df1['Alert'] = df1['Alert'].astype('category').cat.codes
+    df1['Date'] = pd.to_datetime(df1['Date'])
+    df1['DayOfWeek'] = df1['Date'].dt.day_name()
+    df1['HourOfDay'] = df1['Date'].dt.hour
+    df1['Date'] = df1['Date'].astype('category').cat.codes
+    df1['Time'] = pd.to_datetime(df1['Time'], errors='coerce')
+    df1['DayOfWeek'] = df1['DayOfWeek'].astype('category').cat.codes
+    df1['HourOfDay'] = df1['HourOfDay'].astype('category').cat.codes
+    df1.drop(['HourOfDay'], axis=1, inplace=True)
+
+    correlation_matrix = df1.corr()
+
+    # Create a heatmap of the correlation matrix
+    fig2 = ff.create_annotated_heatmap(z=correlation_matrix.values,
+    x=list(correlation_matrix.columns),
+    y=list(correlation_matrix.index),
+    colorscale='Viridis')
+
+    # Update plot layout
+    fig2.update_layout(title='Correlation Between Alert Occurrence and Road Conditions')
+    config = {
+        'scrollZoom': False,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig2, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'corr1', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='650px', width='1500px')]))
+    
+
+
+    
+
+    
+
+
+
+
+
+
+@on('#driver-behaviour-analysis')
+@on('page4_reset')
+async def page4(q: Q):
+    q.page['sidebar'].value = '#driver-behaviour-analysis'
+    # When routing, drop all the cards except of the main ones (header, sidebar, meta).
+    # Since this page is interactive, we want to update its card
+    # instead of recreating it every time, so ignore 'form' card on drop.
+    clear_cards(q, ['form'])
+
+    # Now df_expanded has each industry on a separate row
+
+    # Group the data by alert type and count the occurrences of each alert
+    
+    df_temp = df.copy()
+    alert_counts = df_temp['Alert'].value_counts().reset_index()
+    alert_counts.columns = ['Alert', 'Frequency']
+
+
+    # Create a pie chart of alert frequencies
+    fig = px.pie(alert_counts, values='Frequency', names='Alert', title='Distribution of Driver Alerts')
+    config = {
+        'scrollZoom': False,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'corr1', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='650px', width='1500px')]))
+
+@on('#safety-impact-analysis')
+async def page_target_aud(q: Q):
+    q.page['sidebar'].value = '#safety-impact-analysis'
+    # When routing, drop all the cards except of the main ones (header, sidebar, meta).
+    # Since this page is interactive, we want to update its card
+    # instead of recreating it every time, so ignore 'form' card on drop.
+    clear_cards(q, ['form'])
+
+    
+
+
+    safety_df = df[(df['Alert'] == 'cas_ldw') | (df['Alert'] == 'cas_hmw') | (df['Alert'] == 'hard_brake') |
+               (df['Alert'] == 'cas_pcw') | (df['Alert'] == 'cas_fcw')]
+
+    fig1 = px.scatter(safety_df.groupby('Speed')['Alert'].count().reset_index(), x='Speed', y='Alert',
+                    title='Speed vs. Frequency of Safety-Related Alerts', trendline='ols')
+    fig1.update_layout(xaxis_title='Speed', yaxis_title='Frequency of Safety Alerts')
+
+    config = {
+        'scrollZoom': False,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig1, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'safe1', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='650px', width='1500px')]))
+    # Box plot comparing speeds during safety-related alert events vs non-alert events
+    fig2 = px.box(safety_df, x='Alert', y='Speed', title='Speed Distribution During Safety Alerts')
+    fig2.update_layout(xaxis_title='Alert Type', yaxis_title='Speed')
+    config = {
+        'scrollZoom': False,
+        'showLink': False,
+        'displayModeBar': False
+    }
+    html = pio.to_html(fig2, validate=False, include_plotlyjs='cdn', config=config)
+    add_card(q, 'safe2', ui.form_card(box=ui.box('vertical', width='1500px'), title='', items=[
+        ui.frame(content=html, height='650px', width='1500px')]))
+    
+
+
+
+
+
+
+
+
+
+@on()
+async def page4_step2(q: Q):
+    # Just update the existing card, do not recreate.
+    q.page['form'].items = [
+        ui.stepper(name='stepper', items=[
+            ui.step(label='Step 1', done=True),
+            ui.step(label='Step 2'),
+            ui.step(label='Step 3'),
+        ]),
+        ui.textbox(name='textbox2', label='Textbox 2'),
+        ui.buttons(justify='end', items=[
+            ui.button(name='page4_step3', label='Next', primary=True),
+        ])
+    ]
+
+
+@on()
+async def page4_step3(q: Q):
+    # Just update the existing card, do not recreate.
+    q.page['form'].items = [
+        ui.stepper(name='stepper', items=[
+            ui.step(label='Step 1', done=True),
+            ui.step(label='Step 2', done=True),
+            ui.step(label='Step 3'),
+        ]),
+        ui.textbox(name='textbox3', label='Textbox 3'),
+        ui.buttons(justify='end', items=[
+            ui.button(name='page4_reset', label='Finish', primary=True),
+        ])
+    ]
+
+
+async def init(q: Q) -> None:
+    q.page['meta'] = ui.meta_card(box='', layouts=[ui.layout(breakpoint='xs', min_height='100vh', zones=[
+        ui.zone('main', size='1', direction=ui.ZoneDirection.ROW, zones=[
+            ui.zone('sidebar', size='300px'),
+            ui.zone('body', zones=[
+                ui.zone('header'),
+                ui.zone('content', zones=[
+                    # Specify various zones and use the one that is currently needed. Empty zones are ignored.
+                    ui.zone('horizontal', direction=ui.ZoneDirection.ROW,),
+                    ui.zone('zone2',direction=ui.ZoneDirection.ROW ),
+                    ui.zone('vertical'),
+                    ui.zone('grid', direction=ui.ZoneDirection.ROW, wrap='stretch', justify='center'),
+                    ui.zone(name='zone1', direction=ui.ZoneDirection.ROW),
+                    ui.zone(name='zone3',direction=ui.ZoneDirection.COLUMN)
+                ]),
+            ]),
+        ])
+    ])])
+    q.page['sidebar'] = ui.nav_card(
+        box='sidebar', color='primary', title = 'Advanced Driver Assistance System', subtitle="",
+        value=f'#{q.args["#"]}' if q.args['#'] else '#intro',
+        image='', items=[
+            ui.nav_group('Menu', items=[
+                ui.nav_item(name='#intro', label='Spatial Analysis'),
+                ui.nav_item(name='#data-frame-analysis', label='Data Frame Analysis'),
+                ui.nav_item(name='#alert-frequency-analysis', label='Alert Freuency Analysis'),
+                ui.nav_item(name='#speed-analysis', label='Speed Analysis'),
+                ui.nav_item(name='#correlation-analysis', label='Correlation Analysis'),
+                ui.nav_item(name='#driver-behaviour-analysis', label='Driver Behaviour Analysis'),
+                ui.nav_item(name='#safety-impact-analysis',label='Safety Impact Analysis'),
+                
+            ]),
+        ],
+    )
+    q.page['header'] = ui.header_card(
+        box='header', title='', subtitle='',
+    )
+
+    # If no active hash present, render page1.
+    if q.args['#'] is None:
+        await page_intro(q)
+
+
+@app('/')
+async def serve(q: Q):
+    # Run only once per client connection.
+    if not q.client.initialized:
+        q.client.cards = set()
+        await init(q)
+        q.client.initialized = True
+
+    # Handle routing.
+    await run_on(q)
+    await q.page.save()
diff --git a/adas-data-analysis-dashboards/download.py b/adas-data-analysis-dashboards/download.py
new file mode 100644
index 0000000..39b22c5
--- /dev/null
+++ b/adas-data-analysis-dashboards/download.py
@@ -0,0 +1,44 @@
+import os
+import requests
+from tqdm import tqdm
+
+# Replace these URLs with the actual URLs from your GitHub Release
+
+DATA_URLS = {
+    'iraste_nxt_cas.csv': 'https://drive.google.com/uc?export=download&id=1h6AdKj3U6MiNXFILvKig8j32E3_SGdfG',
+    'iraste_nxt_casdms.csv': 'https://drive.google.com/uc?export=download&id=1Vxl7Zv40NOBMWrsrHP9l8SxzdbNjvktm'
+}
+DATA_DIR = 'data'
+
+def download_file(url, filename):
+    if not os.path.exists(DATA_DIR):
+        os.makedirs(DATA_DIR)
+    
+    filepath = os.path.join(DATA_DIR, filename)
+    
+    if os.path.exists(filepath):
+        print(f"{filename} already exists. Skipping download.")
+        return
+    
+    response = requests.get(url, stream=True)
+    total_size = int(response.headers.get('content-length', 0))
+
+    with open(filepath, 'wb') as file, tqdm(
+        desc=filename,
+        total=total_size,
+        unit='iB',
+        unit_scale=True,
+        unit_divisor=1024,
+    ) as progress_bar:
+        for data in response.iter_content(chunk_size=1024):
+            size = file.write(data)
+            progress_bar.update(size)
+
+def main():
+    for filename, url in DATA_URLS.items():
+        print(f"Downloading {filename}...")
+        download_file(url, filename)
+    print("All files downloaded successfully.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/adas-data-analysis-dashboards/readme.md b/adas-data-analysis-dashboards/readme.md
new file mode 100644
index 0000000..578d22a
--- /dev/null
+++ b/adas-data-analysis-dashboards/readme.md
@@ -0,0 +1,24 @@
+# ADAS Dataset Analysis - Wave App
+
+This repository contains the analysis of the Advanced Driver Assistance System (ADAS) dataset, focusing on understanding the occurrence and characteristics of various alerts generated by vehicles equipped with ADAS. The analysis covers spatial distribution, alert frequency, speed analysis, correlation analysis, driver behavior analysis, and safety impact analysis.
+
+## Dataset Description
+
+The dataset comprises approximately 1.8 million records of ADAS alerts recorded within India. There are 8 types of alerts, including Lane Departure Warning, Headway Monitoring and Warning, Hard Brake, Pedestrian Collision Warning, Forward Collision Warning, Driver Distracted, No Seatbelt, and Smoking while Driving.
+
+### APP Features:
+- The app helps Visualizes the geographical distribution of ADAS alerts across India using a responsive, interactive map.
+![Alt text](static/2.png)
+- The app features loading a data frame that supports interactive search, selection and grouping using h2o-wave components.
+![Alt text](static/df.png)
+- The app features direct integration of interactive Plotly visualizations using HTML. This approach allows for the seamless embedding of complex, interactive plots within the H2O Wave interface, enhancing the user experience and providing rich, explorable data visualizations
+![Alt text](static/4.png)
+
+
+### Instructions to Run the App:
+- Clone the repository
+- Initialize & Activate a virtual environment
+- Install the requirements
+- run download.py to download the data into data directory
+- wave run app.py
+- Open a web browser and navigate to `http://localhost:10101/` to view the app.
\ No newline at end of file
diff --git a/adas-data-analysis-dashboards/requirements.txt b/adas-data-analysis-dashboards/requirements.txt
new file mode 100644
index 0000000..022f5b1
--- /dev/null
+++ b/adas-data-analysis-dashboards/requirements.txt
@@ -0,0 +1,10 @@
+plotly
+pandas
+numpy
+matplotlib
+seaborn
+folium
+scikit-learn
+nbformat==4.2.0
+h2o-wave
+statsmodels
\ No newline at end of file
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