diff --git a/Quantium_certificate.pdf b/Quantium_certificate.pdf
new file mode 100644
index 0000000000..f80e7a1095
Binary files /dev/null and b/Quantium_certificate.pdf differ
diff --git a/README.md b/README.md
index 7ee65c0ab1..327c7e7420 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,40 @@
 # Quantium starter repo
-This repo contains everything you need to get started on the program! Good luck!
+
+__Task1:__
+Setting up the local repo.
+
+__Task2:__
+
+Initial datasets daily_sales_data_0.csv, daily_sales_data_1.csv, daily_sales_data_2.csv
+The attributes in the above datasets are  
+1. product - {'gold morsel', 'magenta morsel', 'periwinkle morsel', 'vermilion morsel', 'chartreuse morsel', 'lapis morsel', 'pink morsel'}
+2. price 
+3. quantity
+4. date
+5. region - {'west', 'east', 'north', 'south'}
+
+Combining the three datasets into a single dataset i.e combined_data.csv. The Soul Foods is only interested in Pink Morsels
+
+The output file contains three fields:
+1. sales = price * quantity
+2. date
+3. region
+
+__Task3:__
+
+Outputs:
+![alt text](newplot.png)
+![alt text](newplot_s.png)
+![alt text](newplot_e.png)
+![alt text](newplot_w.png)
+
+(*Threshold : sales on Jan 15 2021*)
+
+__Task4:__
+
+Added some UI to it so it is convenient to use.
+![alt text](newplot_ui.png)
+
+__Task5:__
+
+Add some unit test cases to verify the Dash application.
\ No newline at end of file
diff --git a/Task3.ipynb b/Task3.ipynb
new file mode 100644
index 0000000000..e6f859c7ee
--- /dev/null
+++ b/Task3.ipynb
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7a281ff7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"650\"\n",
+       "            src=\"http://127.0.0.1:8050/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x19c544cc280>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from dash import Dash, dcc, html\n",
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "app = Dash(__name__)\n",
+    "\n",
+    "#reading csv file\n",
+    "\n",
+    "# Get the absolute path to the data folder\n",
+    "data_folder = os.path.abspath('data')\n",
+    "\n",
+    "# Construct the absolute path to the CSV file\n",
+    "\n",
+    "# Read the CSV file\n",
+    "df_0 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_0.csv'))\n",
+    "\n",
+    "\n",
+    "#filtering only pink morsel\n",
+    "df_0 = df_0[df_0['product'] == 'pink morsel']\n",
+    "\n",
+    "# from price column removing $ and converting to number\n",
+    "df_0['price'] = pd.to_numeric(df_0['price'].str.replace('$', '', regex=False), errors='coerce') \n",
+    "\n",
+    "#adding new column named sales where sales=price*quantity\n",
+    "df_0['sales'] = df_0['price'] * df_0['quantity']\n",
+    "\n",
+    "#dropping the columns\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_0 = df_0.drop(columns=columns_to_drop)\n",
+    "\n",
+    "df_1 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_1.csv'))\n",
+    "\n",
+    "df_1 = df_1[df_1['product'] == 'pink morsel']\n",
+    "\n",
+    "df_1['price'] = pd.to_numeric(df_1['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "df_1['sales'] = df_1['price'] * df_1['quantity']\n",
+    "\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_1 = df_1.drop(columns=columns_to_drop)\n",
+    "\n",
+    "df_2 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_2.csv'))\n",
+    "\n",
+    "df_2 = df_2[df_2['product'] == 'pink morsel']\n",
+    "\n",
+    "df_2['price'] = pd.to_numeric(df_2['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "df_2['sales'] = df_2['price'] * df_2['quantity']\n",
+    "\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_2 = df_2.drop(columns=columns_to_drop)\n",
+    "\n",
+    "# combining all three different datasets into single dataset\n",
+    "combined_df = pd.concat([df_0,df_1,df_2],ignore_index = True)\n",
+    "combined_df = combined_df.drop(columns = 'product')\n",
+    "\n",
+    "# sorting dataset by region \n",
+    "c_north = combined_df[combined_df['region'] == 'north']\n",
+    "c_south = combined_df[combined_df['region'] == 'south']\n",
+    "c_east = combined_df[combined_df['region'] == 'east']\n",
+    "c_west = combined_df[combined_df['region'] == 'west']\n",
+    "\n",
+    "# thresold value for north region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_north = c_north.loc[c_north['date']== '2021-01-15','sales'] \n",
+    "north_range=sales_on_query_data_north.values[0]  if not sales_on_query_data_north.empty else 0\n",
+    "\n",
+    "line_color = ['sales greater than thresold' if y > north_range else 'sales lesser than thresold' for y in c_north['sales']]\n",
+    "c_north.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "# thresold value for south region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_south = c_south.loc[c_south['date']== '2021-01-15','sales'] \n",
+    "south_range=sales_on_query_data_south.values[0]  if not sales_on_query_data_south.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > south_range else 'sales lesser than thresold' for y in c_south['sales']]\n",
+    "c_south.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "# thresold value for east region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_east = c_east.loc[c_east['date']== '2021-01-15','sales'] \n",
+    "east_range=sales_on_query_data_east.values[0]  if not sales_on_query_data_east.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > east_range else 'sales lesser than thresold' for y in c_east['sales']]\n",
+    "c_east.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "# thresold value for west region where threshold=sales on Jan 15, 2021\n",
+    "sales_on_query_data_west = c_west.loc[c_west['date']== '2021-01-15','sales'] \n",
+    "west_range=sales_on_query_data_west.values[0]  if not sales_on_query_data_west.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > west_range else 'sales lesser than thresold' for y in c_west['sales']]\n",
+    "c_west.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "app.layout = html.Div(children=[\n",
+    "    html.H1(\n",
+    "        children='Soul Foods',\n",
+    "        style={\n",
+    "            'textAlign': 'center'\n",
+    "        }\n",
+    "    ),\n",
+    "\n",
+    "    html.Div(children='Sales of Pink Morsel', style={\n",
+    "        'textAlign': 'center',\n",
+    "        'fontSize': '30px'\n",
+    "    }),\n",
+    "    \n",
+    "    html.Div(children='Sales = Price*Quantity',style={\n",
+    "        'border':'2px solid black',\n",
+    "        'margin-right':'800px'\n",
+    "    }),\n",
+    "\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 1',\n",
+    "        figure=px.line(c_north, x='date', y='sales', color='Color',title='North region: Sales on each day')\n",
+    "    ),\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 2',\n",
+    "        figure=px.line(c_south, x='date', y='sales', color='Color',title='South region: Sales on each day')\n",
+    "    ),\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 3',\n",
+    "        figure=px.line(c_east, x='date', y='sales', color='Color',title='East region: Sales on each day')\n",
+    "    ),\n",
+    "    dcc.Graph(\n",
+    "        id='line chart 4',\n",
+    "        figure=px.line (c_west, x='date', y='sales', color='Color',title='West region: Sales on each day')\n",
+    "    )\n",
+    "])\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    app.run(debug=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56f4e847",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Task4.ipynb b/Task4.ipynb
new file mode 100644
index 0000000000..0d0de0e89f
--- /dev/null
+++ b/Task4.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c62d7cfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\harik\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1667: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=\"650\"\n",
+       "            src=\"http://127.0.0.1:8052/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x2133dadea60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from dash import Dash, dcc, html, Input, Output\n",
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "app = Dash(__name__)\n",
+    "\n",
+    "data_folder = os.path.abspath('data')\n",
+    "\n",
+    "# Construct the absolute path to the CSV file\n",
+    "\n",
+    "# Read the CSV file\n",
+    "df_0 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_0.csv'))\n",
+    "\n",
+    "\n",
+    "# Filter rows where 'product' is 'pink morsel'\n",
+    "df_0 = df_0[df_0['product'] == 'pink morsel']\n",
+    "\n",
+    "# Convert 'price' column to numeric\n",
+    "df_0['price'] = pd.to_numeric(df_0['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "# Calculate 'sales' column\n",
+    "df_0['sales'] = df_0['price'] * df_0['quantity']\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_0 = df_0.drop(columns=columns_to_drop)\n",
+    "\n",
+    "# Print the resulting DataFrame\n",
+    "# print(df_0)\n",
+    "\n",
+    "df_1 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_1.csv'))\n",
+    "\n",
+    "# Filter rows where 'product' is 'pink morsel'\n",
+    "df_1 = df_1[df_1['product'] == 'pink morsel']\n",
+    "\n",
+    "# Convert 'price' column to numeric\n",
+    "df_1['price'] = pd.to_numeric(df_1['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "# Calculate 'sales' column\n",
+    "df_1['sales'] = df_1['price'] * df_1['quantity']\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_1 = df_1.drop(columns=columns_to_drop)\n",
+    "\n",
+    "# Print the resulting DataFrame\n",
+    "# print(df_1)\n",
+    "\n",
+    "df_2 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_2.csv'))\n",
+    "\n",
+    "# Filter rows where 'product' is 'pink morsel'\n",
+    "df_2 = df_2[df_2['product'] == 'pink morsel']\n",
+    "\n",
+    "# Convert 'price' column to numeric\n",
+    "df_2['price'] = pd.to_numeric(df_2['price'].str.replace('$', '', regex=False), errors='coerce')\n",
+    "\n",
+    "# Calculate 'sales' column\n",
+    "df_2['sales'] = df_2['price'] * df_2['quantity']\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "columns_to_drop = ['price', 'quantity']\n",
+    "df_2 = df_2.drop(columns=columns_to_drop)\n",
+    "\n",
+    "# Print the resulting DataFrame\n",
+    "\n",
+    "combined_df = pd.concat([df_0,df_1,df_2],ignore_index = True)\n",
+    "combined_df = combined_df.drop(columns = 'product')\n",
+    "# add 4 regions sales value here-------\n",
+    "grouped_df = combined_df.groupby('date')['sales'].sum().reset_index()\n",
+    "\n",
+    "# Merge the grouped DataFrame back to the original DataFrame based on 'Date'\n",
+    "merged_df = pd.merge(combined_df, grouped_df, on='date',suffixes=('', '_total'))\n",
+    "merged_df = merged_df.drop(columns = 'sales')\n",
+    "merged_df=merged_df.drop_duplicates(subset='date',ignore_index=True)\n",
+    "merged_df['region']='all'\n",
+    "\n",
+    "c_north = combined_df[combined_df['region'] == 'north']\n",
+    "c_south = combined_df[combined_df['region'] == 'south']\n",
+    "c_east = combined_df[combined_df['region'] == 'east']\n",
+    "c_west = combined_df[combined_df['region'] == 'west']\n",
+    "\n",
+    "sales_on_query_data_north = c_north.loc[c_north['date']== '2021-01-15','sales'] \n",
+    "north_range=sales_on_query_data_north.values[0]  if not sales_on_query_data_north.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > north_range else 'sales lesser than thresold' for y in c_north['sales']]\n",
+    "c_north.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "sales_on_query_data_south = c_south.loc[c_south['date']== '2021-01-15','sales'] \n",
+    "south_range=sales_on_query_data_south.values[0]  if not sales_on_query_data_south.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > south_range else 'sales lesser than thresold' for y in c_south['sales']]\n",
+    "c_south.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "sales_on_query_data_east = c_east.loc[c_east['date']== '2021-01-15','sales'] \n",
+    "east_range=sales_on_query_data_east.values[0]  if not sales_on_query_data_east.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > east_range else 'sales lesser than thresold' for y in c_east['sales']]\n",
+    "c_east.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "sales_on_query_data_west = c_west.loc[c_west['date']== '2021-01-15','sales'] \n",
+    "west_range=sales_on_query_data_west.values[0]  if not sales_on_query_data_west.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > west_range else 'sales lesser than thresold' for y in c_west['sales']]\n",
+    "c_west.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "sales_on_query_data_all = merged_df.loc[merged_df['date']== '2021-01-15','sales_total'] \n",
+    "all_range=sales_on_query_data_all.values[0]  if not sales_on_query_data_all.empty else 0\n",
+    "line_color = ['sales greater than thresold' if y > all_range else 'sales lesser than thresold' for y in merged_df['sales_total']]\n",
+    "merged_df.loc[:, 'Color'] = line_color\n",
+    "\n",
+    "app.layout = html.Div(children=[\n",
+    "    html.H1(\n",
+    "        children='Soul Foods',\n",
+    "        style={\n",
+    "            'textAlign': 'center'\n",
+    "        }\n",
+    "    ),\n",
+    "\n",
+    "    html.Div(children='Sales after Rise in price of Pink Morsel', id=\"header\",style={\n",
+    "        'textAlign': 'center'\n",
+    "    }),\n",
+    "\n",
+    "    dcc.RadioItems(\n",
+    "        id='region-selector',\n",
+    "        options=[\n",
+    "            {'label': 'North', 'value': 'north'},\n",
+    "            {'label': 'South', 'value': 'south'},\n",
+    "            {'label': 'East', 'value': 'east'},\n",
+    "            {'label': 'West', 'value': 'west'},\n",
+    "            {'label': 'All','value':'all'},\n",
+    "        ],\n",
+    "        value='north',  # Default value\n",
+    "        labelStyle={'display': 'block'}\n",
+    "    ),\n",
+    "\n",
+    "    dcc.Graph(\n",
+    "        id='line-chart',\n",
+    "    )\n",
+    "])\n",
+    "\n",
+    "# Callback to update the line chart based on the selected region\n",
+    "@app.callback(\n",
+    "    Output('line-chart', 'figure'),\n",
+    "    [Input('region-selector', 'value')]\n",
+    ")\n",
+    "\n",
+    "def update_line_chart(selected_region):\n",
+    "    if selected_region == 'all':\n",
+    "        fig = px.line(merged_df, x='date', y='sales_total', color='Color', title='Sales in all regions(threshold = sales on Jan 15, 2021)')\n",
+    "    else:\n",
+    "        region_data = None\n",
+    "        if selected_region == 'north':\n",
+    "            region_data = c_north\n",
+    "        elif selected_region == 'south':\n",
+    "            region_data = c_south\n",
+    "        elif selected_region == 'east':\n",
+    "            region_data = c_east\n",
+    "        elif selected_region == 'west':\n",
+    "            region_data = c_west\n",
+    "\n",
+    "        fig = px.line(region_data, x='date', y='sales', color='Color', title=f'Sales in {selected_region} region(threshold = sales on Jan 15, 2021)')\n",
+    "\n",
+    "    return fig\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    app.run(debug=True,port=8052)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55b484f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Task4.py b/Task4.py
new file mode 100644
index 0000000000..6812a4feb6
--- /dev/null
+++ b/Task4.py
@@ -0,0 +1,176 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+from dash import Dash, dcc, html, Input, Output
+import plotly.express as px
+import pandas as pd
+import os
+
+app = Dash(__name__)
+
+data_folder = os.path.abspath('data')
+
+# Construct the absolute path to the CSV file
+
+# Read the CSV file
+df_0 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_0.csv'))
+
+
+# Filter rows where 'product' is 'pink morsel'
+df_0 = df_0[df_0['product'] == 'pink morsel']
+
+# Convert 'price' column to numeric
+df_0['price'] = pd.to_numeric(df_0['price'].str.replace('$', '', regex=False), errors='coerce')
+
+# Calculate 'sales' column
+df_0['sales'] = df_0['price'] * df_0['quantity']
+
+# Drop unnecessary columns
+columns_to_drop = ['price', 'quantity']
+df_0 = df_0.drop(columns=columns_to_drop)
+
+# Print the resulting DataFrame
+# print(df_0)
+
+df_1 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_1.csv'))
+
+# Filter rows where 'product' is 'pink morsel'
+df_1 = df_1[df_1['product'] == 'pink morsel']
+
+# Convert 'price' column to numeric
+df_1['price'] = pd.to_numeric(df_1['price'].str.replace('$', '', regex=False), errors='coerce')
+
+# Calculate 'sales' column
+df_1['sales'] = df_1['price'] * df_1['quantity']
+
+# Drop unnecessary columns
+columns_to_drop = ['price', 'quantity']
+df_1 = df_1.drop(columns=columns_to_drop)
+
+# Print the resulting DataFrame
+# print(df_1)
+
+df_2 = pd.read_csv(os.path.join(data_folder, 'daily_sales_data_2.csv'))
+
+# Filter rows where 'product' is 'pink morsel'
+df_2 = df_2[df_2['product'] == 'pink morsel']
+
+# Convert 'price' column to numeric
+df_2['price'] = pd.to_numeric(df_2['price'].str.replace('$', '', regex=False), errors='coerce')
+
+# Calculate 'sales' column
+df_2['sales'] = df_2['price'] * df_2['quantity']
+
+# Drop unnecessary columns
+columns_to_drop = ['price', 'quantity']
+df_2 = df_2.drop(columns=columns_to_drop)
+
+# Print the resulting DataFrame
+
+combined_df = pd.concat([df_0,df_1,df_2],ignore_index = True)
+combined_df = combined_df.drop(columns = 'product')
+# add 4 regions sales value here-------
+grouped_df = combined_df.groupby('date')['sales'].sum().reset_index()
+
+# Merge the grouped DataFrame back to the original DataFrame based on 'Date'
+merged_df = pd.merge(combined_df, grouped_df, on='date',suffixes=('', '_total'))
+merged_df = merged_df.drop(columns = 'sales')
+merged_df=merged_df.drop_duplicates(subset='date',ignore_index=True)
+merged_df['region']='all'
+
+c_north = combined_df[combined_df['region'] == 'north']
+c_south = combined_df[combined_df['region'] == 'south']
+c_east = combined_df[combined_df['region'] == 'east']
+c_west = combined_df[combined_df['region'] == 'west']
+
+sales_on_query_data_north = c_north.loc[c_north['date']== '2021-01-15','sales'] 
+north_range=sales_on_query_data_north.values[0]  if not sales_on_query_data_north.empty else 0
+line_color = ['sales greater than thresold' if y > north_range else 'sales lesser than thresold' for y in c_north['sales']]
+c_north.loc[:, 'Color'] = line_color
+
+sales_on_query_data_south = c_south.loc[c_south['date']== '2021-01-15','sales'] 
+south_range=sales_on_query_data_south.values[0]  if not sales_on_query_data_south.empty else 0
+line_color = ['sales greater than thresold' if y > south_range else 'sales lesser than thresold' for y in c_south['sales']]
+c_south.loc[:, 'Color'] = line_color
+
+sales_on_query_data_east = c_east.loc[c_east['date']== '2021-01-15','sales'] 
+east_range=sales_on_query_data_east.values[0]  if not sales_on_query_data_east.empty else 0
+line_color = ['sales greater than thresold' if y > east_range else 'sales lesser than thresold' for y in c_east['sales']]
+c_east.loc[:, 'Color'] = line_color
+
+sales_on_query_data_west = c_west.loc[c_west['date']== '2021-01-15','sales'] 
+west_range=sales_on_query_data_west.values[0]  if not sales_on_query_data_west.empty else 0
+line_color = ['sales greater than thresold' if y > west_range else 'sales lesser than thresold' for y in c_west['sales']]
+c_west.loc[:, 'Color'] = line_color
+
+sales_on_query_data_all = merged_df.loc[merged_df['date']== '2021-01-15','sales_total'] 
+all_range=sales_on_query_data_all.values[0]  if not sales_on_query_data_all.empty else 0
+line_color = ['sales greater than thresold' if y > all_range else 'sales lesser than thresold' for y in merged_df['sales_total']]
+merged_df.loc[:, 'Color'] = line_color
+
+app.layout = html.Div(children=[
+    html.H1(
+        children='Soul Foods',
+        style={
+            'textAlign': 'center'
+        }
+    ),
+
+    html.Div(children='Sales after Rise in price of Pink Morsel', id="header",style={
+        'textAlign': 'center'
+    }),
+
+    dcc.RadioItems(
+        id='region-selector',
+        options=[
+            {'label': 'North', 'value': 'north'},
+            {'label': 'South', 'value': 'south'},
+            {'label': 'East', 'value': 'east'},
+            {'label': 'West', 'value': 'west'},
+            {'label': 'All','value':'all'},
+        ],
+        value='north',  # Default value
+        labelStyle={'display': 'block'}
+    ),
+
+    dcc.Graph(
+        id='line-chart',
+    )
+])
+
+# Callback to update the line chart based on the selected region
+@app.callback(
+    Output('line-chart', 'figure'),
+    [Input('region-selector', 'value')]
+)
+
+def update_line_chart(selected_region):
+    if selected_region == 'all':
+        fig = px.line(merged_df, x='date', y='sales_total', color='Color', title='Sales in all regions(threshold = sales on Jan 15, 2021)')
+    else:
+        region_data = None
+        if selected_region == 'north':
+            region_data = c_north
+        elif selected_region == 'south':
+            region_data = c_south
+        elif selected_region == 'east':
+            region_data = c_east
+        elif selected_region == 'west':
+            region_data = c_west
+
+        fig = px.line(region_data, x='date', y='sales', color='Color', title=f'Sales in {selected_region} region(threshold = sales on Jan 15, 2021)')
+
+    return fig
+
+if __name__ == '__main__':
+    app.run(debug=True,port=8052)
+
+
+# In[ ]:
+
+
+
+
diff --git a/Task5.ipynb b/Task5.ipynb
new file mode 100644
index 0000000000..7c6e31b69a
--- /dev/null
+++ b/Task5.ipynb
@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6aaae077",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\harik\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1667: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from Task4 import app\n",
+    "def test_header(dash_duo):\n",
+    "    dash_duo.start_server(app)\n",
+    "    dash_duo.wait_for_element(\"#header\", timeout=10)\n",
+    "\n",
+    "\n",
+    "def test_line_chart(dash_duo):\n",
+    "    dash_duo.start_server(app)\n",
+    "    dash_duo.wait_for_element(\"#line-chart\", timeout=10)\n",
+    "\n",
+    "\n",
+    "def test_region_selector(dash_duo):\n",
+    "    dash_duo.start_server(app)\n",
+    "    dash_duo.wait_for_element(\"#region-selector\", timeout=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab9685d1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/combined_data.csv b/data/combined_data.csv
new file mode 100644
index 0000000000..ef68911e1d
--- /dev/null
+++ b/data/combined_data.csv
@@ -0,0 +1,5881 @@
+date,region,sales
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