Interactive data science project that analyzes and compares grocery prices across Karachi stores using Python, EDA, and Linear Regression, with a Streamlit dashboard for visualizing price trends and store-wise comparisons. 🛒 Grocery Price Comparison & Trend Analysis for Karachi 📌 Project Overview
Grocery prices in Karachi vary significantly across different online stores, making it difficult for consumers to identify cost-effective options for essential items. This project focuses on collecting, cleaning, analyzing, and visualizing grocery price data from multiple online retailers operating in Karachi and presenting insights through an interactive Streamlit dashboard.
The project is developed as part of the Introduction to Data Science Lab (CSL-495) at Bahria University, Karachi Campus.
🎯 Objectives
Collect grocery price data from multiple online stores
Clean and standardize price and unit information
Perform Exploratory Data Analysis (EDA) to compare prices
Visualize price trends across stores and categories
Apply a basic Linear Regression model to observe short-term price trends
Build an interactive Streamlit web app for data exploration
🧰 Technologies & Tools
Python
Pandas & NumPy – Data cleaning and processing
Matplotlib – Data visualization
Scikit-learn – Linear Regression model
Streamlit – Interactive dashboard
CSV Dataset – Price data storage
📊 Key Features 🔹 Data Collection
Manual price collection of essential grocery items
Limited web scraping from publicly available product pages
Unified dataset stored in CSV format
🔹 Data Cleaning & Processing
Removal of duplicate records
Conversion of units (kg, g, ml, pcs) into standard grams
Calculation of price per kg for fair comparison
🔹 Exploratory Data Analysis (EDA)
Store-wise price comparison
Category-wise price analysis
Visualizations including:
Bar charts
Line charts
Box plots
🔹 Price Trend Prediction
Linear Regression applied on selected grocery items
Visualization of historical prices vs predicted trends
Model evaluation using MSE and R² Score
🔹 Streamlit Dashboard
Interactive filters for:
Store
Category
Item
Real-time charts and statistics
Download filtered dataset as CSV
🖥️ Streamlit App Preview
The project includes a fully functional Streamlit web application that allows users to:
Compare grocery prices across stores
Analyze category-wise pricing
Visualize historical price trends
Predict short-term price movements
To run the app locally:
streamlit run app.py